LLM

Welcome to the world of open-source (LLMs) large language models, where the future of technology meets community spirit. By breaking down the barriers of proprietary systems, open language models invite developers, researchers, and enthusiasts from around the globe to contribute to, modify, and improve upon the foundational models.

This collaborative spirit not only accelerates advancements in the field but also ensures that the benefits of AI technology are accessible to a broader audience. As we navigate through the intricacies of open-source language models, we’ll uncover the challenges and opportunities that come with adopting an open-source model, the ecosystems that support these endeavors, and the real-world applications that are transforming industries.

Benefits of open-source LLMs

As soon as ChatGPT was revealed, OpenAI’s GPT models quickly rose to prominence. However, businesses began to recognize the high costs associated with closed-source models, questioning the value of investing in large models that lacked specific knowledge about their operations.

In response, many opted for smaller open LLMs, utilizing Retriever-And-Generator (RAG) pipelines to integrate their data, achieving comparable or even superior efficiency.

There are several advantages to closed-source large language models worth considering.

1. Cost-effectiveness:

Open-source Large Language Models (LLMs) present a cost-effective alternative to their proprietary counterparts, offering organizations a financially viable means to harness AI capabilities.

• No licensing fees are required, significantly lowering initial and ongoing expenses.
• Organizations can freely deploy these models, leading to direct cost reductions.
• Open large language models allow for specific customization, enhancing efficiency without the need for vendor-specific customization services.

2. Flexibility:

Companies are increasingly preferring the flexibility to switch between open and proprietary (closed) models to mitigate risks associated with relying solely on one type of model.

This flexibility is crucial because a model provider’s unexpected update or failure to keep the model current can negatively affect a company’s operations and customer experience.

Companies often lean towards open language models when they want more control over their data and the ability to fine-tune models for specific tasks using their data, making the model more effective for their unique needs.

3. Data ownership and control:

Companies leveraging open-source language models gain significant control and ownership over their data, enhancing security and compliance through various mechanisms. Here’s a concise overview of the benefits and controls offered by using open large language models:

Data hosting control:

• Choice of data hosting on-premises or with trusted cloud providers.
• Crucial for protecting sensitive data and ensuring regulatory compliance.

Internal data processing:

• Avoids sending sensitive data to external servers.
• Reduces the risk of data breaches and enhances privacy.

Customizable data security features:

• Flexibility to implement data anonymization and encryption.
• Helps comply with data protection laws like GDPR and CCPA.

Transparency and audibility:

• The open-source nature allows for code and process audits.
• Ensures alignment with internal and external compliance standards.

Examples of enterprises leveraging open-source LLMs

Here are examples of how different companies around the globe have started leveraging open language models.

1. VMWare

VMWare, a noted enterprise in the field of cloud computing and digitalization, has deployed an open language model called the HuggingFace StarCoder. Their motivation for using this model is to enhance the productivity of their developers by assisting them in generating code.

This strategic move suggests VMware’s priority for internal code security and the desire to host the model on their infrastructure. It contrasts with using an external system like Microsoft-owned GitHub’s Copilot, possibly due to sensitivities around their codebase and not wanting to give Microsoft access to it

2. Brave

Brave, the security-focused web browser company, has deployed an open-source large language model called Mixtral 8x7B from Mistral AI for their conversational assistant named Leo, which aims to differentiate the company by emphasizing privacy.

Previously, Leo utilized the Llama 2 model, but Brave has since updated the assistant to default to the Mixtral 8x7B model. This move illustrates the company’s commitment to integrating open LLM technologies to maintain user privacy and enhance their browser’s functionality.

3. Gab Wireless

Gab Wireless, the company focused on child-friendly mobile phone services, is using a suite of open-source models from Hugging Face to add a security layer to its messaging system. The aim is to screen the messages sent and received by children to ensure that no inappropriate content is involved in their communications. This usage of open language models helps Gab Wireless ensure safety and security in children’s interactions, particularly with individuals they do not know.

4. IBM

IBM actively incorporates open models across various operational areas.

• AskHR application: Utilizes IBM’s Watson Orchestration and open language models for efficient HR query resolution.
• Consulting advantage tool: Features a “Library of Assistants” powered by IBM’s wasonx platform and open-source large language models, aiding consultants.
• Marketing initiatives: Employs an LLM-driven application, integrated with Adobe Firefly, for innovative content and image generation in marketing.

5. Intuit

Intuit, the company behind TurboTax, QuickBooks, and Mailchimp, has developed its language models incorporating open LLMs into the mix. These models are key components of Intuit Assist, a feature designed to help users with customer support, analysis, and completing various tasks. The company’s approach to building these large language models involves using open-source frameworks, augmented with Intuit’s unique, proprietary data.

6. Shopify

Shopify has employed publically available language models in the form of Shopify Sidekick, an AI-powered tool that utilizes Llama 2. This tool assists small business owners with automating tasks related to managing their commerce websites. It can generate product descriptions, respond to customer inquiries, and create marketing content, thereby helping merchants save time and streamline their operations.

7. LyRise

LyRise, a U.S.-based talent-matching startup, utilizes open language models by employing a chatbot built on Llama, which operates similarly to a human recruiter. This chatbot assists businesses in finding and hiring top AI and data talent, drawing from a pool of high-quality profiles in Africa across various industries.

8. Niantic

Niantic, known for creating Pokémon Go, has integrated open-source large language models into its game through the new feature called Peridot. This feature uses Llama 2 to generate environment-specific reactions and animations for the pet characters, enhancing the gaming experience by making character interactions more dynamic and context-aware.

9. Perplexity

Here’s how Perplexity leverages open-source LLMs

• Response generation process:

When a user poses a question, Perplexity’s engine executes approximately six steps to craft a response. This process involves the use of multiple language models, showcasing the company’s commitment to delivering comprehensive and accurate answers.

In a crucial phase of response preparation, specifically the second-to-last step, Perplexity employs its own specially developed open-source language models. These models, which are enhancements of existing frameworks like Mistral and Llama, are tailored to succinctly summarize content relevant to the user’s inquiry.

The fine-tuning of these models is conducted on AWS Bedrock, emphasizing the choice of open models for greater customization and control. This strategy underlines Perplexity’s dedication to refining its technology to produce superior outcomes.

• Partnership and API integration:

Expanding its technological reach, Perplexity has entered into a partnership with Rabbit to incorporate its open-source large language models into the R1, a compact AI device. This collaboration facilitated through an API, extends the application of Perplexity’s innovative models, marking a significant stride in practical AI deployment.

10. CyberAgent

CyberAgent, a Japanese digital advertising firm, leverages open language models with its OpenCALM initiative, a customizable Japanese language model enhancing its AI-driven advertising services like Kiwami Prediction AI. By adopting an open-source approach, CyberAgent aims to encourage collaborative AI development and gain external insights, fostering AI advancements in Japan. Furthermore, a partnership with Dell Technologies has upgraded their server and GPU capabilities, significantly boosting model performance (up to 5.14 times faster), thereby streamlining service updates and enhancements for greater efficiency and cost-effectiveness.

Challenges of open-source LLMs

While open LLMs offer numerous benefits, there are substantial challenges that can plague the users.

1. Customization necessity:

Open language models often come as general-purpose models, necessitating significant customization to align with an enterprise’s unique workflows and operational processes. This customization is crucial for the models to deliver value, requiring enterprises to invest in development resources to adapt these models to their specific needs.

2. Support and governance:

Unlike proprietary models that offer dedicated support and clear governance structures, publically available large language models present challenges in managing support and ensuring proper governance. Enterprises must navigate these challenges by either developing internal expertise or engaging with the open-source community for support, which can vary in responsiveness and expertise.

3. Reliability of techniques:

Techniques like Retrieval-Augmented Generation aim to enhance language models by incorporating proprietary data. However, these techniques are not foolproof and can sometimes introduce inaccuracies or inconsistencies, posing challenges in ensuring the reliability of the model outputs.

4. Language support:

While proprietary models like GPT are known for their robust performance across various languages, open-source large language models may exhibit variable performance levels. This inconsistency can affect enterprises aiming to deploy language models in multilingual environments, necessitating additional effort to ensure adequate language support.

5. Deployment complexity:

Deploying publically available language models, especially at scale, involves complex technical challenges. These range from infrastructure considerations to optimizing model performance, requiring significant technical expertise and resources to overcome.

6. Uncertainty and risk:

Relying solely on one type of model, whether open or closed source, introduces risks such as the potential for unexpected updates by the provider that could affect model behavior or compliance with regulatory standards.

7. Legal and ethical considerations:

Deploying LLMs entails navigating legal and ethical considerations, from ensuring compliance with data protection regulations to addressing the potential impact of AI on customer experiences. Enterprises must consider these factors to avoid legal repercussions and maintain trust with their users.

8. Lack of public examples:

The scarcity of publicly available case studies on the deployment of publically available LLMs in enterprise settings makes it challenging for organizations to gauge the effectiveness and potential return on investment of these models in similar contexts.

Overall, while there are significant potential benefits to using publically available language models in enterprise settings, including cost savings and the flexibility to fine-tune models, addressing these challenges is critical for successful deployment

Embracing open-source LLMs: A path to innovation and flexibility

In conclusion, open-source language models represent a pivotal shift towards more accessible, customizable, and cost-effective AI solutions for enterprises. They offer a unique blend of benefits, including significant cost savings, enhanced data control, and the ability to tailor AI tools to specific business needs, while also presenting challenges such as the need for customization and navigating support complexities.

Through the collaborative efforts of the global open-source community and the innovative use of these models across various industries, enterprises are finding new ways to leverage AI for growth and efficiency.

However, success in this endeavor requires a strategic approach to overcome inherent challenges, ensuring that businesses can fully harness the potential of publically available LLMs to drive innovation and maintain a competitive edge in the fast-evolving digital landscape.

In the dynamic world of artificial intelligence, strides in innovation are commonplace. At the forefront of these developments is Mistral AI, a European company emerging as a strong contender in the Large Language Models (LLM) arena with its latest offering: Mistral Large. With capabilities meant to rival industry giants, Mistral AI is poised to leave a significant imprint on the tech landscape.

 

Features of Mistral AI’s large model

 

Mistral AI’s new flagship model, codenamed Mistral Large, isn’t just a mere ripple in the AI pond; it’s a technological tidal wave. As we take a look at what sets it apart, let’s compare the main features and capabilities of Mistral AI’s Large model, as detailed in the sources, with those commonly attributed to GPT-4.

 

 

Language support

Mistral Large: Natively fluent in English, French, Spanish, German, and Italian.
GPT-4: is known for supporting multiple languages, but the exact list isn’t specified in the sources.

 

Scalability

Mistral Large: Offers different versions, including Mistral Small for lower latency and cost optimization.
GPT-4: Provides various scales of models, but specific details on versions aren’t provided in the sources.

 

Training and cost

Mistral Large: Charges $8 per million input tokens and $24 per million output tokens.
GPT-4: Mistral Large is noted to be 20% cheaper than GPT-4 Turbo, which suggests GPT-4 would be more expensive.

 

Performance on benchmarks

Mistral Large: Claims to rank second after GPT-4 on commonly used benchmarks and only marginally outperforms offerings from Google and Meta under the MMLU benchmark.

GPT-4

It is known to be one of the leading models in terms of benchmark performance, but no specific details on benchmark scores are provided in the sources.

Cost to train

Mistral Large: The model reportedly cost less than $22 million to train.
GPT-4: cost over $100 million to develop, according to claims.

Multilingual Abilities

Le Chat supports a variety of languages including English, French, Spanish, German, and Italian 1.

Different Versions

Users can choose between three different models, namely Mistral Small, Mistral Large, and Mistral Next, the latter of which is designed to be brief and concise.

Web Access

Currently, Le Chat does not have the capability to access the internet 1.

Free Beta Access

Le Chat is available in a beta version that is free for users, requiring just a sign-up to use 2.

Planned Enterprise Version

Mistral AI plans to offer a paid version for enterprise clients with features like central billing and the ability to define moderation mechanisms

Please note that this comparison is based on the information provided within the sources, which may not include all features and capabilities of GPT-4 or Mistral Large.

 

Mistral AI vs. GPT-4: A comparative look

 

 

Against the backdrop of OpenAI’s GPT-4 stands Mistral Large, challenging the status quo with outstanding features. While GPT-4 shines with its multi-language support and high benchmark performance, Mistral Large offers a competitive edge through:

 

Affordability: It’s 20% cheaper than GPT-4 Turbo, negotiating cost-savings for AI-powered projects.

 

Benchmark Performance: Mistral Large competes closely with GPT-4, ranking just behind it while surpassing other tech behemoths in several benchmarks.

 

Multilingual Prowess: Exceptionally fluent across English, French, Spanish, German, and Italian, Mistral Large breaks language barriers with ease.

 

Efficiency in Development: Crafted with capital efficiency in mind, Mistral AI invested less than $22 million in training its model, a fraction of the cost incurred by its counterparts.

 

Commercially Savvy: The model offers a paid API with usage-based pricing, balancing accessibility with a monetized business strategy, presenting a cost-effective solution for developers and businesses.

 

 

Practical applications of Mistral AI’s Large and GPT-4

 

The applications of both Mistral AI’s Large and GPT-4 sprawl across various industries and use cases, such as:

 

Natural Language Understanding: Both models demonstrate excellence in understanding and generating human-like text, pushing the boundaries of conversational AI.

 

Multilingual Support: Business expansion and global communication are facilitated through the multilingual capabilities of both LLMs.

 

Code Generation: Their ability to understand and generate code makes them invaluable tools for software developers and engineers.

 

Recommendations for use

 

As businesses and individuals navigate through the options in large language models, here’s why you might consider each tool:

 

Choose Mistral AI’s Large: If you’re looking for a cost-effective solution with efficient multilingual support and the flexibility of scalable versions to suit different needs 2.

 

Opt for GPT-4: Should your project require the prestige and robustness associated with OpenAI’s cutting-edge research and model performance, GPT-4 remains an industry benchmark 3.

 

 

Final note

 

In conclusion, while both Mistral AI’s Large and GPT-4 stand as pioneers in their own right, the choice ultimately aligns with your specific requirements and constraints. With Mistral AI nipping at the heels of OpenAI, the world of AI remains an exciting space to watch.

 

The march of AI is relentless, and as Mistral AI parallels the giants in the tech world, make sure to keep abreast of their developments, for the choice you make today could redefine your technological trajectory tomorrow.

Are you confused about where to start working on your large language model? It all starts with an understanding of a typical LLM project lifecycle. As part of the generative AI world, LLMs have led to innovation in machine-learning tasks.

 

Let’s take a look at the steps that make up an LLM project lifecycle and their impact on the process.

 

Roadmap to understanding an LLM project lifecycle

 

Within the realm of generative AI, a project involving large language models can be a daunting task. It demands proper coordination and skills to execute a task successfully. In order to create an ease of understanding, we have broken down a typical LLM project lifecycle into multiple steps.

 

 

In this section, we will delve deeper into the various stages of the process.

 

Defining the scope of the project

 

It is paramount to begin your LLM project lifecycle by understanding its scope. It begins with a comprehension of the problem you aim to solve. Market research and stakeholder interviews are a good place to start at this stage. You must also review the available technological possibilities.

 

LLMs are multifunctional but the size and architecture of the model determine its ability, ranging from long-form text generation and text summarization to language translation. Based on your research, you can determine the specifics of your LLM project and hence the scope of it.

 

The next part of this step is to explore the feasibility of a solution in generative AI. You must use this to set clear and measurable objectives as they would define the roadmap for your LLM project lifecycle.

 

Data preprocessing and relevant considerations

 

Now that you have defined your problem, the next step is to look for relevant data. Data collection can encompass various sources, depending on your problem. Once you have the data, you need to clean and preprocess it. The goal is to make the data usable for model training.

 

Moreover, it is important in your LLM project lifecycle to consider all the ethical and legal considerations when dealing with data. You must have the clearance to use data, including protection laws, anonymization, and user consent. Moreover, you must ensure the prevention of potential biases through the diversity of perspectives in the data.

 

 

Selecting a relevant model

 

When it comes to model selection, you have two choices. Either use an existing base model or pre-train your own from scratch. Based on your project demands, you can start by exploring the available models to check if any aligns with your requirements.

 

Models like GPT-4 and PalM2 are powerful model options. Moreover, you can also explore FLAN-T5 – a hugging face model, offering enhanced Text-to-Text Transfer Transformer features. However, you need to consider license and certification details before choosing an open-source base model.

 

In case none of the existing models fulfill your demands, you need to pre-train a model from scratch to begin your LLM project lifecycle. It requires machine-learning expertise, computational resources, and time. The large investment in pre-training results in a highly customized model for your project.

 

• What is pre-training? It is a compute-intensive phase of unsupervised learning tasks. In an LLM project lifecycle, the objective primarily focuses on text generation or next-token prediction. During this complex process, the model is trained and the transformer architecture is decided. It results in the creation of Formation Models.

 

Training the model

 

The next step in the LLM project lifecycle is to adapt and train the foundation model. The goal is to refine your LLM model with your project requirements. Let’s look at some common techniques for the model training process.

 

• Prompt engineering: As the name suggests, this method relies on prompt generation. You must structure prompts carefully for your LLM model to get accurate results. It requires you to have a proper understanding of your model and the project goals.

For a typical LLM model, a prompt is provided to the model for it to generate a text. This complete process is called inference. It is the simplest phase in an LLM project lifecycle that aims to refine your model responses and enhance its performance.

 

• Fine-tuning: At this point, you focus on customizing your model to your specific project needs. The fine-tuning process enables you to convert a generic model into a tailored one by using domain-specific data, resulting in its optimized performance for particular tasks. It is a supervised learning task that adds weights to the foundation model, making it more efficient in the process.

 

• Caching: It is one of the less-renowned but important techniques in the training process. It involves the frequent storage of prompts and responses to speed up your model’s performance. Caching high-dimensional vectors results in faster retrieval of information and generation of more efficient results.

 

Reinforcement learning

 

Reinforcement learning happens from human or AI feedback, where the former is called RLHF and the latter is RLAIF. RLHF is aimed at aligning the LLM model with human values, expectations, and standards. The human evaluators review, rate, and provide feedback on the model performance.

 

 

It is an iterative process completed using rewards against each successful model output which results in the creation of a rewards model. Then the RLAIF is used to scale human feedback that ensures the model is completely aligned with the human values.

 

 

Evaluating the model

 

It involves the validation and testing of your LLM model. The model is tested using unseen data (also referred to as test data). The output is evaluated against a set of metrics. Some common LLM evaluation metrics include BLEU (Bilingual Evaluation Understudy), GLUE (General Language Understanding Evaluation), and HELM (Holistic Evaluation of Language Models).

 

Along with the set metrics, the results are also analyzed for adherence to ethical standards and the absence of biases. This ensures that your model for the LLM project lifecycle is efficient and relevant to your goals.

 

Model optimization and deployment

 

Model optimization is a prerequisite to the deployment process. You must ensure that the model is efficiently designed for your application environment. The process primarily includes the reduction of model size, enhancement of inference speed, and efficient operation of the model in real-world scenarios. It ensures faster inference using less memory.

 

Some common optimization techniques include:

 

• Distillation – it teaches a smaller model (called the student model) from a larger model (called the teacher model)

 

• Post-training quantization – it aims to reduce the precision of model weights

 

• Pruning – it focuses on removing the model weights that have negligible impact

 

This stage of the LLM project lifecycle concludes with seamless integration of workflows, existing systems, and architectures. It ensures smooth accessibility and operation of the model.

 

Model monitoring and building LLM applications

 

The LLM project lifecycle does not end at deployment. It is crucial to monitor the model’s performance in real-world situations and ensure its adaptability to evolving requirements. It also focuses on addressing any issues that arise and regularly updating the model parameters.

 

Finally, your model is ready for building robust LLM applications. These platforms can cater to diverse goals, including automated content creation, advanced predictive analysis, and other solutions to complex problems.

 

 

Summarizing the LLM project lifecycle

Hence, the roadmap to completing an LLM project lifecycle is a complex trajectory involving multiple stages. Each stage caters to a unique aspect of the model development process. The final goal is to create a customized and efficient machine-learning model to deploy and build innovative LLM applications.

Large Language Models have surged in popularity due to their remarkable ability to understand, generate, and interact with human language with unprecedented accuracy and fluency.

This surge is largely attributed to advancements in machine learning and the vast increase in computational power, enabling these models to process and learn from billions of words and texts on the internet.

OpenAI significantly shaped the landscape of LLMs with the introduction of GPT-3.5, marking a pivotal moment in the field. Unlike its predecessors, GPT-3.5 was not fully open-source, giving rise to closed-source large language models.

This move was driven by considerations around control, quality, and the commercial potential of such powerful models. OpenAI’s approach showcased the potential for proprietary models to deliver cutting-edge AI capabilities while also igniting discussions about accessibility and innovation.

The introduction of open-source LLM 

Contrastingly, companies like Meta and Mistral have opted for a different approach by releasing models like LLaMA and Mistral as open-source.

These models not only challenge the dominance of closed-source models like GPT-3.5 but also fuel the ongoing debate over which approach—open-source or closed-source—yields better results. Read more

By making their models openly available, Meta and similar entities encourage widespread innovation, allowing researchers and developers to improve upon these models, which in turn, has seen them topping performance leaderboards.

From an enterprise standpoint, understanding the differences between open-source LLM and closed-source LLM is crucial. The choice between the two can significantly impact an organization’s ability to innovate, control costs, and tailor solutions to specific needs.

Let’s dig in to understand the difference between Open-Source LLM and Closed Source LLM

What are open-source large language models?

Open-source large language models, such as the ones offered by Meta AI, provide a foundational AI technology that can analyze and generate human-like text by learning from vast datasets consisting of various written materials.

As open-source software, these language models have their source code and underlying architecture publicly accessible, allowing developers, researchers, and enterprises to use, modify, and distribute them freely.

Let’s dig into different features of open-sourced large language models

1. Community contributions

• Broad participation:

  Open-source projects allow anyone to contribute, from individual hobbyists to researchers and developers from various industries. This diversity in the contributor base brings a wide array of perspectives, skills, and needs into the project.

• Innovation and problem-solving:

  Different contributors may identify unique problems or have innovative ideas for applications that the original developers hadn’t considered. For example, someone might improve the model’s performance on a specific language or dialect, develop a new method for reducing bias, or create tools that make the model more accessible to non-technical users.

2. Wide range of applications

• Specialized use cases:

  Contributors often adapt and extend open-source models for specialized use cases. For instance, a developer might fine-tune a language model on legal documents to create a tool that assists in legal research or on medical literature to support healthcare professionals.

• New features and enhancements:

  Through experimenting with the model, contributors might develop new features, such as more efficient training algorithms, novel ways to interpret the model’s outputs, or integration capabilities with other software tools.

3. Iterative improvement and evolution

• Feedback loop:

  The open-source model encourages a cycle of continuous improvement. As the community uses and experiments with the model, they can identify shortcomings, bugs, or opportunities for enhancement. Contributions addressing these points can be merged back into the project, making the model more robust and versatile over time.

• Collaboration and knowledge sharing:

  Open-source projects facilitate collaboration and knowledge sharing within the community. Contributions are often documented and discussed publicly, allowing others to learn from them, build upon them, and apply them in new contexts.

4. Examples of open-sourced large language models

• Meta’s LLaMA 2
• Bloom by Hugging Face
• Mixtral of Experts by Mistral

What are closed-source large language models?

Closed-source large language models, such as GPT-3.5 by OpenAI, embody advanced AI technologies capable of analyzing and generating human-like text through learning from extensive datasets.

Unlike their open-source counterparts, the source code and architecture of closed-source language models are proprietary, accessible only under specific terms defined by their creators. This exclusivity allows for controlled development, distribution, and usage.

Features of closed-sourced large language models

1. Controlled quality and consistency

• Centralized development: Closed-source projects are developed, maintained, and updated by a dedicated team, ensuring a consistent quality and direction of the project. This centralized approach facilitates the implementation of high standards and systematic updates.
• Reliability and stability: With a focused team of developers, closed-source LLMs often offer greater reliability and stability, making them suitable for enterprise applications where consistency is critical.

2. Commercial support and innovation

• Vendor support: Closed-source models come with professional support and services from the vendor, offering assistance for integration, troubleshooting, and optimization, which can be particularly valuable for businesses.
• Proprietary innovations:  The controlled environment of closed-source development enables the introduction of unique, proprietary features and improvements, often driving forward the technology’s frontier in specialized applications.

3. Exclusive use and intellectual property

• Competitive advantage: The proprietary nature of closed-source language models allows businesses to leverage advanced AI capabilities as a competitive advantage, without revealing the underlying technology to competitors.
• Intellectual property protection: Closed-source licensing protects the intellectual property of the developers, ensuring that their innovations remain exclusive and commercially valuable.

4. Customization and integration

• Tailored solutions: While customization in closed-source models is more restricted than in open-source alternatives, vendors often provide tailored solutions or allow certain levels of configuration to meet specific business needs.
• Seamless integration: Closed-source large language models are designed to integrate smoothly with existing systems and software, providing a seamless experience for businesses and end-users.

Examples of closed-source large language Models

1. GPT 3.5 by OpenAI
2. Gemini by Google
3. Claude by Anthropic

 

Read: Should Large Language Models be Open-Sourced? Stepping into the Biggest Debates

 

Open-source and closed-source language models for enterprise adoption:

 

In terms of enterprise adoption, comparing open-source and closed-source large language models involves evaluating various factors such as costs, innovation pace, support, customization, and intellectual property rights. While I can’t directly access external sources like the VentureBeat article you mentioned, I can provide a general comparison based on known aspects of how enterprises use these models:

Costs

• Open-Source: Generally offers lower initial costs since there are no licensing fees for the software itself. However, enterprises may incur costs related to infrastructure, development, and potentially higher operational costs due to the need for in-house expertise to customize, maintain, and update the models.
• Closed-Source: Often involves licensing fees, subscription costs, or usage-based pricing, which can predictably scale with use. While the initial and ongoing costs can be higher, these models frequently come with vendor support, reducing the need for extensive in-house expertise and potentially lowering overall maintenance and operational costs.

Innovation and updates

• Open-Source: The pace of innovation can be rapid, thanks to contributions from a diverse and global community. Enterprises can benefit from the continuous improvements and updates made by contributors. However, the direction of innovation may not always align with specific enterprise needs.
• Closed-Source: Innovation is managed by the vendor, which can ensure that updates are consistent and high-quality. While the pace of innovation might be slower compared to the open-source community, it’s often more predictable and aligned with enterprise needs, especially for vendors closely working with their client base.

Support and reliability

• Open-Source: Support primarily comes from the community, forums, and potentially from third-party vendors offering professional services. While there can be a wealth of shared knowledge, response times and the availability of help can vary.
• Closed-Source: Typically comes with professional support from the vendor, including customer service, technical support, and even dedicated account management. This can ensure reliability and quick resolution of issues, which is crucial for enterprise applications.

Customization and flexibility

• Open-Source: Offer high levels of customization and flexibility, allowing enterprises to modify the models to fit their specific needs. This can be particularly valuable for niche applications or when integrating the model into complex systems.
• Closed-Source: Customization is usually more limited compared to open-source models. While some vendors offer customization options, changes are generally confined to the parameters and options provided by the vendor.

Intellectual property and competitive advantage

• Open-Source: Using open-source models can complicate intellectual property (IP) considerations, especially if modifications are shared publicly. However, they allow enterprises to build proprietary solutions on top of open technologies, potentially offering a competitive advantage through innovation.
• Closed-Source: The use of closed-source models clearly defines IP rights, with enterprises typically not owning the underlying technology. However, leveraging cutting-edge, proprietary models can provide a different type of competitive advantage through access to exclusive technologies.

Choosing Between Open-Source LLMs and Closed-Source LLMs

The choice between open-source and closed-source language models for enterprise adoption involves weighing these factors in the context of specific business objectives, resources, and strategic directions.

Open-source models can offer cost advantages, customization, and rapid innovation but require significant in-house expertise and management. Closed-source models provide predictability, support, and ease of use at a higher cost, potentially making them a more suitable choice for enterprises looking for ready-to-use, reliable AI solutions.

The race of big tech and startups to create the top language model has us eager to see how things change.

Different companies are training new models to achieve better accuracy, enhanced understanding of context, and more nuanced generation capabilities, pushing the boundaries of what AI can achieve in terms of natural language understanding and generation.

A standout approach in this field is employed by Mistral AI through its development of the Mixtral model.

Distinctive for its use of the Sparse Mixture of Experts (SMoE) technique, Mixtral amalgamates the expertise of various specialized models. Each of these models excels in different areas of data processing, enabling Mixtral to navigate the complexities of language with notable precision.

This article aims to provide an in-depth examination of Mixtral, including its operational framework, unique attributes, and performance metrics. We will explore how Mixtral differentiates itself from other models in the market and the advantages it offers.

How does Mixtral work; What is so unique in its framework?

The Mixtral 8x7B model is a smart tool that’s built to be really good at a bunch of different tasks. It does this by not using all its tools at once, but just a few at a time for each piece of information it looks at.

Think of it like a toolbox where, out of 8 tools, it picks the best 2 for the job at hand. Each layer of Mixtral has these 8 special tools or “experts,” and it chooses which ones to use based on what it’s working on. This way, it can be really efficient and do its job well without needing to use everything it has all at once.

The process from the input through the router to the expert and the resulting output works as follows:

Input: A given input vector, representing a token from a sequence, enters the model. Each token is processed individually by going through the layers of the model. The input is part of a larger context, which can be a span of up to 32k tokens. Read how embeddings work here.

Router: After the initial input, the router within the Mixture of Experts layer determines which experts to engage for processing the token. Specifically, the router selects 2 out of the 8 available experts based on the token’s characteristics. This selection is done using a gating network that assigns weights to the experts, guiding which experts are to be used.

Experts: Once the experts are selected by the router, the input token is processed by these experts. Each expert consists of a standard feedforward block as found in a transformer architecture. The outputs of the two chosen experts are then combined through a weighted sum, where the weights are determined by the gating network’s output.

Output: The final output for the token is the combined result from the two experts it was routed to. Essentially, the output of the MoE layer is the weighted sum of the outputs of the expert networks.

This process is repeated for each token within the sequence, allowing the Mixtral model to effectively process and generate the response or continuation based on the input it receives.

Unique Attributes of Mixtral’s Approach

1. High Temporal Locality

The interesting part is that Mixtral tends to pick the same expert or group of experts for words that are close together or related in some way i.e. the model possesses “high temporal locality”.

It’s like noticing that a certain part of your game has a lot of jumping, so you stick with the character who’s best at jumping for that whole section.

The implications of such high temporal locality are substantial for both training and inference efficiency. It suggests that expert assignments can be somewhat predicted over time, providing opportunities to optimize the model’s training and runtime performance.

For instance, the predictability in expert utilization can lead to more efficient caching strategies, wherein the outputs of frequently used experts are temporarily stored, thus speeding up computations for consecutive tokens that are routed to the same experts.

2. Computational Efficiency via Dual Expert Strategy

Mixtral uses only two out of eight experts to handle each piece of data it processes. This selective engagement is key for its computational efficiency, allowing it to work as fast as a model with 12 billion parameters, even though it has four times as many parameters in total.

Performance of Mixtral

Mixtral 8x7B is compared directly with Llama 2 70B and GPT-3.5 and is found to perform similarly or above these models in benchmarks. Specifically, it scores higher on MMLU and does exceptionally well on MT-Bench.

 

Hallucinations and Bias

In comparison with Llama 2, Mixtral exhibits reduced bias in the BBQ benchmark. Furthermore, it tends to show a more favorable outlook than Llama 2 in the BOLD benchmark, while maintaining comparable variations across different aspects.

 Multilingualism

Mixtral vastly outperforms Llama 2 70B on multilingual benchmarks, demonstrating its strength in understanding and generating text across different languages

Charting the Future: Mixtral’s Revolutionary Path in AI Efficiency and Multilinguality

Mistral AI’s Mixtral model has carved out a niche for itself, showcasing the power and precision of the Sparse Mixture of Experts approach. As we’ve navigated through the intricacies of Mixtral, from its unique architecture to its standout performances on various benchmarks, it’s clear that this model is not just another entrant in the race to AI supremacy. It’s a harbinger of a nuanced, efficient future in large language models.

By strategically deploying only two of its eight available experts for each input token, Mixtral achieves a balance between computational efficiency and deep, nuanced understanding that few models can claim. This approach not only enhances processing speed but also reduces bias and improves performance across languages, setting a new standard for what AI can achieve.

As we conclude our exploration of the Genius of Mixtral of Experts by Mistral AI, it’s evident that this model represents a significant leap forward. Through its adept handling of complex language tasks, Mixtral stands as a testament to the potential of combining specialized expertise with smart, scalable architecture. The future of AI looks brighter with Mixtral paving the way, promising models that are not only more efficient and versatile but also more understanding of the vast tapestry of human language.

In the ever-evolving landscape of natural language processing (NLP), embedding techniques have played a pivotal role in enhancing the capabilities of language models.

 

The birth of word embeddings

 

Before venturing into the large number of embedding techniques that have emerged in the past few years, we must first understand the problem that led to the creation of such techniques.

 

Word embeddings were created to address the absence of efficient text representations for NLP models. Since NLP techniques operate on textual data, which inherently cannot be directly integrated into machine learning models designed to process numerical inputs, a fundamental question arose: how can we convert text into a format compatible with these models?

 

Basic approaches like one-hot encoding and Bag-of-Words (BoW) were employed in the initial phases of NLP development. However, these methods were eventually discarded due to their evident shortcomings in capturing the contextual and semantic nuances of language. Each word was treated as an isolated unit, without understanding its relationship with other words or its usage in different contexts.

 

 

Word2Vec 

 

In 2013, Google presented a new technique to overcome the shortcomings of the previous word embedding techniques, called Word2Vec. It represents words in a continuous vector space, better known as an embedding space, where semantically similar words are located close to each other.

 

This contrasted with traditional methods, like one-hot encoding, which represents words as sparse, high-dimensional vectors. The dense vector representations generated by Word2Vec had several advantages, including the ability to capture semantic relationships, support vector arithmetic (e.g., “king” – “man” + “woman” = “queen”), and improve the performance of various NLP tasks like language modeling, sentiment analysis, and machine translation.

 

Transition to GloVe and FastText

 

The success of Word2Vec paved the way for further innovations in the realm of word embeddings. The Global Vectors for Word Representation (GloVe) model, introduced by Stanford researchers in 2014, aimed to leverage global statistical information about word co-occurrences.

 

GloVe demonstrated improved performance over Word2Vec in capturing semantic relationships. Unlike Word2Vec, GloVe considers the entire corpus when learning word vectors, leading to a more global understanding of word relationships.

 

Fast forward to 2016, Facebook’s FastText introduced a significant shift by considering sub-word information. Unlike traditional word embeddings, FastText represented words as bags of character n-grams. This sub-word information allowed FastText to capture morphological and semantic relationships in a more detailed manner, especially for languages with rich morphology and complex word formations. This approach was particularly beneficial for handling out-of-vocabulary words and improving the representation of rare words.

 

The rise of transformer models 

 

The real game-changer in the evolution of embedding techniques came with the advent of the Transformer architecture. Introduced by researchers at Google in the form of the Attention is All You Need paper in 2017, Transformers demonstrated remarkable efficiency in capturing long-range dependencies in sequences.

 

The architecture laid the foundation for state-of-the-art models like OpenAI’s GPT (Generative Pre-trained Transformer) series and BERT (Bidirectional Encoder Representations from Transformers). Hence, the traditional understanding of embedding techniques is revamped with new solutions.

 

Impact of embedding techniques on language models

 

The embedding techniques mentioned above have significantly impacted the performance and capabilities of LLMs. Pre-trained models like GPT-3 and BERT leverage these embeddings to understand natural language context, semantics, and syntactic structures. The ability to capture context allows these models to excel in a wide range of NLP tasks, including sentiment analysis, text summarization, and question-answering.

 

Imagine the sentence: “The movie was not what I expected, but the plot twist at the end made it incredible.”

 

Traditional models might struggle with the negation of “not what I expected.” Word embeddings could capture some sentiment but might miss the subtle shift in sentiment caused by the positive turn of events in the latter part of the sentence.

 

In contrast, LLMs with contextualized embeddings can consider the entire sentence and comprehend the nuanced interplay of positive and negative sentiments. They grasp that the initial negativity is later counteracted by the positive twist, resulting in a more accurate sentiment analysis.

 

Advantages of embeddings in LLMs

 

• Contextual Understanding: LLMs equipped with embeddings comprehend the context in which words appear, allowing for a more nuanced interpretation of sentiment in complex sentences.

 

• Semantic Relationships: Word embeddings capture semantic relationships between words, enabling the model to understand the subtleties and nuances of language. 

 

• Handling Ambiguity: Contextual embeddings help LLMs handle ambiguous language constructs, such as negations or sarcasm, contributing to improved accuracy in sentiment analysis.

 

• Transfer Learning: The pre-training of LLMs with embeddings on vast datasets allows them to generalize well to various downstream tasks, including sentiment analysis, with minimal task-specific data.

 

How are enterprises using embeddings in their LLM processes?

 

In light of recent advancements, enterprises are keen on harnessing the robust capabilities of Large Language Models (LLMs) to construct comprehensive Software as a Service (SAAS) solutions. Nevertheless, LLMs come pre-trained on extensive datasets, and to tailor them to specific use cases, fine-tuning on proprietary data becomes essential.

 

This process can be laborious. To streamline this intricate task, the widely embraced Retrieval Augmented Generation (RAG) technique comes into play. RAG involves retrieving pertinent information from an external source, transforming it to a format suitable for LLM comprehension, and then inputting it into the LLM to generate textual output.

 

This innovative approach enables the fine-tuning of LLMs with knowledge beyond their original training scope. In this process, you need an efficient way to store, retrieve, and ingest data into your LLMs to use it accurately for your given use case.

 

One of the most common ways to store and search over unstructured data is to embed it and store the resulting embedding vectors, and then at query time to embed the unstructured query and retrieve the embedding vectors that are ‘most similar’ to the embedded query.  Hence, without embedding techniques, your RAG approach will be impossible.

 

 

Understanding the creation of embeddings

 

Much like a machine learning model, an embedding model undergoes training on extensive datasets. Various models available can generate embeddings for you, and each model is distinct. You can find the top embedding models here.

 

It is unclear what makes an embedding model perform better than others. However, a common way to select one for your use case is to evaluate how many words a model can take in without breaking down. There’s a limit to how many tokens a model can handle at once, so you’ll need to split your data into chunks that fit within the limit. Hence, choosing a suitable model is a good starting point for your use case.

 

Creating embeddings with Azure OpenAI is a matter of a few lines of code. To create embeddings of a simple sentence like The food was delicious and the waiter…, you can execute the following code blocks:

 

• First, import AzureOpenAI from OpenAI

 

• Load in your environment variables

 

• Create your Azure OpenAI client.

 

• Create your embeddings

 

And you’re done! It’s really that simple to generate embeddings for your data. If you want to generate embeddings for an entire dataset, you can follow along with the great notebook provided by OpenAI itself here.

 

 

To sum it up!

 

The evolution of embedding techniques has revolutionized natural language processing, empowering language models with a deeper understanding of context and semantics. From Word2Vec to Transformer models, each advancement has enriched LLM capabilities, enabling them to excel in various NLP tasks.

 

Enterprises leverage techniques like Retrieval Augmented Generation, facilitated by embeddings, to tailor LLMs for specific use cases. Platforms like Azure OpenAI offer straightforward solutions for generating embeddings, underscoring their importance in NLP development. As we forge ahead, embeddings will remain pivotal in driving innovation and expanding the horizons of language understanding.

Imagine staring at a blank screen, the cursor blinking impatiently. You know you have a story to tell, but the words just won’t flow. You’ve brainstormed, outlined, and even consumed endless cups of coffee, but inspiration remains elusive. This was often the reality for writers, especially in the fast-paced world of blog writing.

In this struggle, enter chatbots as potential saviors, promising to spark ideas with ease. But their responses often felt generic, trapped in a one-size-fits-all format that stifled creativity. It was like trying to create a masterpiece with a paint-by-numbers kit.

Then comes Dynamic Few-Shot Prompting into the scene. This revolutionary technique is a game-changer in the creative realm, empowering language models to craft more accurate, engaging content that resonates with readers.

It addresses the challenges by dynamically selecting a relevant subset of examples for prompts, allowing for a tailored and diverse set of creative responses specific to user needs. Think of it as having access to a versatile team of writers, each specializing in different styles and genres.

Quick prompting test for you

 

To comprehend this exciting technique, let’s first delve into its parent concept: Few-shot prompting.

Few-Shot Prompting

Few-shot prompting is a technique in natural language processing that involves providing a language model with a limited set of task-specific examples, often referred to as “shots,” to guide its responses in a desired way. This means you can “teach” the model how to respond on the fly simply by showing it a few examples of what you want it to do.

In this approach, the user collects examples representing the desired output or behavior. These examples are then integrated into a prompt instructing the Large Language Model (LLM) on how to generate the intended responses.

The prompt, including the task-specific examples, is then fed into the LLM, allowing it to leverage the provided context to produce new and contextually relevant outputs.

 

 

Unlike zero-shot prompting, where the model relies solely on its pre-existing knowledge, few-shot prompting enables the model to benefit from in-context learning by incorporating specific task-related examples within the prompt.

 

Dynamic few-shot prompting: Taking it to the next level

Dynamic Few-Shot Prompting takes this adaptability a step further by dynamically selecting the most relevant examples based on the specific context of a user’s query. This means the model can tailor its responses even more precisely, resulting in more relevant and engaging content.

To choose relevant examples, various methods can be employed. In this blog, we’ll explore the semantic example selector, which retrieves the most relevant examples through semantic matching. 

 

What is the importance of dynamic few-shot prompting? 

The significance of Dynamic Few-Shot Prompting lies in its ability to address critical challenges faced by modern Large Language Models (LLMs). With limited context lengths in LLMs, processing longer prompts becomes challenging, requiring increased computational resources and incurring higher financial costs.

Dynamic Few-Shot Prompting optimizes efficiency by strategically utilizing a subset of training data, effectively managing resources. This adaptability allows the model to dynamically select relevant examples, catering precisely to user queries, resulting in more precise, engaging, and cost-effective responses.  

A closer look (with code!)

It’s time to get technical! Let’s delve into the workings of Dynamic Few-Shot Prompting using the LangChain Framework.

Importing necessary modules and libraries.

 

In the .env file, I have my OpenAI API key and base URL stored for secure access.

 

 

This code defines an example prompt template with input variables “user_query” and “blog_format” to be utilized in the FewShotPromptTemplate of LangChain.

 

user_query_1 = “Write a technical blog on topic [user topic]”    blog_format_1 = “””  **Title:** [Compelling and informative title related to user topic]    **Introduction:**  * Introduce the topic in a clear and concise way.  * State the problem or question that the blog will address.  * Briefly outline the key points that will be covered.    **Body:**  * Break down the topic into well-organized sections with clear headings.  * Use bullet points, numbered lists, and diagrams to enhance readability.  * Provide code examples or screenshots where applicable.  * Explain complex concepts in a simple and approachable manner.  * Use technical terms accurately, but avoid jargon that might alienate readers.    **Conclusion:**  * Summarize the main takeaways of the blog.  * Offer a call to action, such as inviting readers to learn more or try a new technique.    **Additional tips for technical blogs:**  * Use visuals to illustrate concepts and break up text.  * Link to relevant resources for further reading.  * Proofread carefully for accuracy and clarity.  “””

user_query_2 = “Write a humorous blog on topic [user topic]”    blog_format_2 = “””  **Title:** [Witty and attention-grabbing title that makes readers laugh before they even start reading]    **Introduction:**  * Set the tone with a funny anecdote or observation.  * Introduce the topic with a playful twist.  * Tease the hilarious insights to come.    **Body:**  * Use puns, wordplay, exaggeration, and unexpected twists to keep readers entertained.  * Share relatable stories and experiences that poke fun at everyday life.  * Incorporate pop culture references or current events for added relevance.  * Break the fourth wall and address the reader directly to create a sense of connection.    **Conclusion:**  * End on a high note with a punchline or final joke that leaves readers wanting more.  * Encourage readers to share their own funny stories or experiences related to the topic.    **Additional tips for humorous blogs:**  * Keep it light and avoid sensitive topics.  * Use visual humor like memes or GIFs.  * Read your blog aloud to ensure the jokes land.  “””

user_query_3 = “Write an adventure blog about a trip to [location]”    blog_format_3 = “””  **Title:** [Evocative and exciting title that captures the spirit of adventure]    **Introduction:**  * Set the scene with vivid descriptions of the location and its atmosphere.  * Introduce the protagonist (you or a character) and their motivations for the adventure.  * Hint at the challenges and obstacles that await.    **Body:**  * Chronicle the journey in chronological order, using sensory details to bring it to life.  * Describe the sights, sounds, smells, and tastes of the location.  * Share personal anecdotes and reflections on the experience.  * Build suspense with cliffhangers and unexpected twists.  * Capture the emotions of excitement, fear, wonder, and accomplishment.    **Conclusion:**  * Reflect on the lessons learned and the personal growth experienced during the adventure.  * Inspire readers to seek out their own adventures.    **Additional tips for adventure blogs:**  * Use high-quality photos and videos to showcase the location.  * Incorporate maps or interactive elements to enhance the experience.  * Write in a conversational style that draws readers in.  “””

 

These examples showcase different blog formats, each tailored to a specific genre. The three dummy examples include a technical blog template with a focus on clarity and code, a humorous blog template designed for entertainment with humor elements, and an adventure blog template emphasizing vivid storytelling and immersive details about a location.

While these are just three examples for simplicity, more formats can be added, to cater to diverse writing styles and topics. Instead of examples showcasing formats, original blogs can also be utilized as examples.

 

 

Next, we’ll compile a list from the crafted examples. This list will be passed to the example selector to store them in the vector store with vector embeddings. This arrangement enables semantic matching to these examples at a later stage.

 

 

Now initialize AzureOpenAIEmbeddings() for creating embeddings used in semantic similarity. 

 

 

Now comes the example selector that stores the provided examples in a vector store. When a user asks a question, it retrieves the most relevant example based on semantic similarity. In this case, k=1 ensures only one relevant example is retrieved.

 

 

This code sets up a FewShotPromptTemplate for dynamic few-shot prompting in LangChain. The ExampleSelector is used to fetch relevant examples based on semantic similarity, and these examples are incorporated into the prompt along with the user query. The resulting template is then ready for generating dynamic and tailored responses.

 

Output

 

 

This output gives an understanding of the final prompt that our LLM will use for generating responses. When the user query is “I’m writing a blog on Machine Learning. What topics should I cover?”, the ExampleSelector employs semantic similarity to fetch the most relevant example, specifically a template for a technical blog.

 

Hence the resulting prompt integrates instructions, the retrieved example, and the user query, offering a customized structure for crafting engaging content related to Machine Learning. With k=1, only one example is retrieved to shape the response.

 

 

As our prompt is ready, now we will initialize an Azure ChatGPT model to generate a tailored blog structure response based on a user query using dynamic few-shot prompting.

 

 

Output

 

 

The LLM efficiently generates a blog structure tailored to the user’s query, adhering to the format of technical blogs, and showcasing how dynamic few-shot prompting can provide relevant and formatted content based on user input.   

 

 

Conclusion

To conclude, Dynamic Few-Shot Prompting takes the best of two worlds (few-shot prompts and zero-shot prompts) and makes language models even better. It helps them understand your goals using smart examples, focusing only on relevant things according to the user’s query. This saves resources and opens the door for innovative use.

Dynamic Few-Shot Prompting adapts well to the token limitations of Large Language Models (LLMs) giving efficient results. As this technology advances, it will revolutionize the way Large Language Models respond, making them more efficient in various applications. 

In today’s world of AI, we’re seeing a big push from both new and established tech companies to build the most powerful language models. Startups like OpenAI and big tech like Google are all part of this competition.

They are creating huge models, like OpenAI’s GPT-4, which has an impressive 1.76 trillion parameters, and Google’s Gemini, which also has a ton of parameters.

But the question arises, is it optimal to always increase the size of the model to make it function well? In other words, is scaling the model always the most helpful choice given how expensive it is to train the model on such huge amounts of data?

Well, this question isn’t as simple as it sounds because making a model better doesn’t just come down to adding more training data.

There have been different studies that show that increasing the size of the model leads to different challenges altogether. In this blog, we’ll be mainly focusing on the inverse scaling.

The Allure of Big Models

Perception of large models equating to better models

The general perception that larger models equate to better performance stems from observed trends in AI and machine learning. As language models increase in size – through more extensive training data, advanced algorithms, and greater computational power – they often demonstrate enhanced capabilities in understanding and generating human language.

This improvement is typically seen in their ability to grasp nuanced context, generate more coherent and contextually appropriate responses, and perform a wider array of complex language tasks.

Consequently, the AI field has often operated under the assumption that scaling up model size is a straightforward path to improved performance. This belief has driven much of the development and investment in ever-larger language models.

However, there are several theories that challenge this notion. Let us explore the concept of inverse scaling and different scenarios where inverse scaling is in action.

Inverse Scaling in Language Models

Inverse scaling is a phenomenon observed in language models. It is a situation where the performance of a model improves with the increase in the scale of data and model size, but beyond a certain point, further scaling leads to a decrease in performance.

Several reasons fuel the inverse scaling process including:

1. Strong Prior

Strong Prior is a key reason for inverse scaling in larger language models. It refers to the tendency of these models to heavily rely on patterns and information they have learned during training.

This can lead to issues such as the Memo Trap, where the model prefers repeating memorized sequences rather than following new instructions.

A strong prior in large language models makes them more susceptible to being tricked due to their over-reliance on patterns learned during training. This reliance can lead to predictable responses, making it easier for users to manipulate the model to generate specific or even inappropriate outputs.

For instance, the model might be more prone to following familiar patterns or repeating memorized sequences, even when these responses are not relevant or appropriate to the given task or context. This can result in the model deviating from its intended function, demonstrating a vulnerability in its ability to adapt to new and varied inputs.

1. Memo Trap

 

Example of Memo Trap

 

This task examines if larger language models are more prone to “memorization traps,” where relying on memorized text hinders performance on specific tasks.

Larger models, being more proficient at modeling their training data, might default to producing familiar word sequences or revisiting common concepts, even when prompted otherwise.

This issue is significant as it highlights how strong memorization can lead to failures in basic reasoning and instruction-following. A notable example is when a model, despite being asked to generate positive content, ends up reproducing harmful or biased material due to its reliance on memorization. This demonstrates a practical downside where larger LMs might unintentionally perpetuate undesirable behavior.

2. Unwanted Imitation

“Unwanted Imitation” in larger language models refers to the models’ tendency to replicate undesirable patterns or biases present in their training data.

As these models are trained on vast and diverse datasets, they often inadvertently learn and reproduce negative or inappropriate behaviors and biases found in the data.

This replication can manifest in various ways, such as perpetuating stereotypes, generating biased or insensitive responses, or reinforcing incorrect information.

The larger the model, the more data it has been exposed to, potentially amplifying this issue. This makes it increasingly challenging to ensure that the model’s outputs remain unbiased and appropriate, particularly in complex or sensitive contexts.

3. Distractor Task

The concept of “Distractor Task” refers to a situation where the model opts for an easier subtask that appears related but does not directly address the main objective.

In such cases, the model might produce outputs that seem relevant but are actually off-topic or incorrect for the given task.

This tendency can be a significant issue in larger models, as their extensive training might make them more prone to finding and following these simpler paths or patterns, leading to outputs that are misaligned with the user’s actual request or intention. Here’s an example:

The correct answer should be ‘pigeon’ because a beagle is indeed a type of dog.

This mistake happens because, even though these larger programs can understand the question format, they fail to grasp the ‘not’ part of the question. So, they’re getting distracted by the easier task of associating ‘beagle’ with ‘dog’ and missing the actual point of the question, which is to identify what a beagle is not.

4. Spurious Few-Shot:

In few-shot learning, a model is given a small number of examples (shots) to learn from and generalize its understanding to new, unseen data. The idea is to teach the model to perform a task with as little prior information as possible.

However, “Spurious Few-Shot” occurs when the few examples provided to the model are misleading in some way, leading the model to form incorrect generalizations or outputs. These examples might be atypical, biased, or just not representative enough of the broader task or dataset. As a result, the model learns the wrong patterns or rules from these examples, causing it to perform poorly or inaccurately when applied to other data.

In this task, the few-shot examples are designed with a correct answer but include a misleading pattern: the sign of the outcome of a bet always matches the sign of the expected value of the bet. This pattern, however, does not apply across all possible examples within the broader task set

Beyond size: future of intelligent learning models

Diving into machine learning, we’ve seen that bigger isn’t always better with something called inverse scaling. Think about it like this: even with super smart computer programs, doing tasks like spotting distractions, remembering quotes wrong on purpose, or copying bad habits can really trip them up. This shows us that even the fanciest programs have their limits and it’s not just about making them bigger. It’s about finding the right mix of size, smarts, and the ability to adapt.

In a world of large language models (LLMs), deep double descent has created a new shift in understanding data and its position in deep learning models. A traditional LLM uses large amounts of data to train a machine-learning model, believing that bigger datasets lead to greater accuracy of results.

 

While OpenAI‘s GPT, Anthropic’s Claude, and Google’s Gemini are focused on using large amounts of training data for improved performance, the recent phenomenon of deep double descent presents an alternative picture. It makes you wonder about the significance of data in modern deep learning.

 

Let’s dig deeper into understanding this phenomenon and its new perspective on the use of large datasets for model training.

 

What is deep double descent?

 

It is a modern phenomenon in deep neural networks that presents its performance as a function of model complexity. Typically, a model improves its performance up to a certain point with an increasing amount of data. Beyond this point, the model output is expected to degrade due to overfitting.

 

The concept of double descent highlights that the performance of a model increases beyond the dip due to overfitting, and then degrades again. Hence, a neural network’s performance experiences a second descent with increasing data complexity.

 

 

A typical pattern of deep double descent can be categorized as follows:

 

• Underparametrized region – refers to the early stages of model training when the parameters are small in number. As the dataset increases in complexity, the model performance is enhanced, resulting in a decrease in the test error.

 

• Overparametrized region – as the model training continues, the number of parameters increases. The increase in data complexity leads to model overfitting, resulting in the degradation of its performance.

 

• Double descent region – it relates to the region beyond the overfitting of the training model. A further increase in data complexity increases the parameters for training, causing a second descent in test error that leads to enhancement in model performance.

 

The name of the phenomenon is rooted in the two descents of test error. The region towards the left of the interpolation point is called the classical regime. In this part, the bias-variance trade-off behaves expectedly. The region towards the right of the interpolation regime. In this region, the model perfectly memorizes the points of training data.

 

 

Understanding the learning lifecycle of a model through double descent

 

As explained in an OpenAI article in 2019, the learning lifecycle of a training model can be explained using the double descent phenomenon. It explains how the test error varies during the iterations of a model’s testing and training.

Let’s look at the three main scenarios of the lifecycle and how each one impacts the training model.

 

Model-wise double descent

 

 

The scenario describes a phenomenon where the model is underparametrized. The model requires more parameters and complexity for improvement in results. A peak in test error occurs around the interpolation point and the model becomes large enough to fit the train set. It also indicates that changes in data complexity, like optimization algorithm, label noise, and the number of training sets can also impact the interpolation threshold and consequently the test error peak.

 

Sample-wise non-monotonicity

 

 

It is the region where an increase in dataset and parameters degrades the model performance. The increase in samples requires larger models to fit the training model, moving the interpolation point to the right. It can be visualized with a shrunken area under the curve that also shifts towards the right.

 

Epoch-wise double descent

 

 

It explains the transition of large models from under to over-parametrized regions. During this time, considerably large training models can experience double descent of the test error. As the number of epochs (training time) is increased, the effect of overfitting is reversed.

 

Hence, the phenomenon highlights how an increase in a dataset can damage model performance before improving it. It raises an important aspect of the deep model learning process, highlighting the importance of data choice for training. Since the optimization of the training process is crucial, it is essential to consider the deep double descent during model training.

 

Are small language models a solution?

 

Since the double descent phenomenon indicates a degraded performance of training models with an increase in data, it has opened a new area of exploration for researchers. Data scientists need to dig deeper into this concept to understand the reasons for the two dips in test errors with larger datasets.

 

While the research is ongoing, there must be other solutions to consider. One such alternative can be in the form of small language models (SLMs). As they work with lowered data complexity and fewer parameters, they offer a solution where an increase in test errors and model degradation can be avoided. It can serve as an alternative solution while research continues to understand the recent phenomenon of double descent.

Retrieval augmented generation (RAG) has improved the function of large language models (LLM). It empowers generative AI to create more coherent and contextually relevant content. Let’s take a deeper look into understanding RAG.

 

What is retrieval augmented generation?

 

It is an AI framework and a type of natural language processing (NLP) model that enables the retrieval of information from an external knowledge base. It integrates retrieval-based and generation-based approaches to provide a robust database for LLMs.

 

A retrieval augmented generation model accesses a large pre-existing pool of knowledge to improve the quality of LLM-generated responses. It ensures that the information is more accurate and up-to-date by combining factual data with contextually relevant information.

 

By combining vector databases and LLM, the retrieval model has set up a standard for the search and navigation of data for generative AI. It has become one of the most used techniques for LLM.

 

 

Benefits of RAG

While retrieval augmented generation improves LLM responses, it offers multiple benefits to the generative AI efforts of an organization.

Explore RAG and its benefits, trade-offs, use cases, and enterprise adoption, in detail with our podcast! 

Improved contextual awareness

 

The retrieval component allows access to a large knowledge base, enabling the model to generate contextually relevant information. Due to improved awareness of the context, the output generated is more coherent and appropriate.

 

Enhanced accuracy

 

An LLM using a retrieval model can produce accurate results with proper attribution, including citations of relevant sources. Access to a large and accurate database ensures that factually correct results are generated.

 

Adaptability to dynamic knowledge

 

The knowledge base of a retrieval model is regularly updated to ensure access to the latest information. The system integrates new information without retraining the entire program, ensuring quick adaptability. It enables the generative models to access the latest statistics and research.

 

Resource efficiency

 

Retrieval mechanisms enable the model to retrieve information from a large information base. The contextual relevance of the data enhances the accuracy of the results, making the process resource-efficient. It makes handling of large data volumes easier and makes the system cost-efficient.

 

Increased developer control

 

Developers use a retrieval augmented generation model to control the information base of a LLM. They can adapt the data to the changing needs of the user. Moreover, they can also restrict the accessibility of the knowledge base, giving them control of data authorization.

 

 

Frameworks for retrieval augmented generation

 

A RAG system combines a retrieval model with a generation model. Developers use frameworks and libraries available online to implement the required retrieval system. Let’s take a look at some of the common resources used for it.

 

Hugging face transformers

 

It is a popular library of pre-trained models for different tasks. It includes retrieval models like Dense Passage Retrieval (DPR) and generation models like GPT. The transformer allows the integration of these systems to generate a unified retrieval augmented generation model.

 

Facebook AI similarity search (FAISS)

 

FAISS is used for similarity search and clustering dense vectors. It plays a crucial role in building retrieval components of a system. Its use is preferred in models where vector similarity is crucial for the system.

 

PyTorch and TensorFlow

 

These are commonly used deep learning frameworks that offer immense flexibility in building RAG models. They enable the developers to create retrieval and generation models separately. Both models can then be integrated into a larger framework to develop a RAG model.

 

Haystack

 

It is a Python framework that is built on Elasticsearch. It is suitable to build end-to-end conversational AI systems. The components of the framework are used for storage of information, retrieval models, and generation models.

 

 

Use cases of RAG

 

Some common use cases and real-world applications are listed below.

Content creation

 

It primarily deals with writing articles and blogs. It is one of the most common uses of LLM where the retrieval models are used to generate coherent and relevant content. It can lead to personalized results for users that include real-time trends and relevant contextual information.

 

Real-time commentary

 

A retriever uses APIs to connect real-time information updates with an LLM. It is used to create a virtual commentator which can be integrated further to create text-to-speech models. IBM used this mechanism during the US Open 2023 for live commentary.

 

Question answering system

 

 

The ability of LLMs to generate contextually relevant content enables the retrieval model to function as a question-answering machine. It can retrieve factual information from an extensive knowledge base to create a comprehensive answer.

 

Language translation

 

Translation is a tricky process. A retrieval model can detect the context of phrases and words, enabling the generation of relevant translations. Access to external databases ensures the results are accurate and fluent for the users. The extensive information on available idioms and phrases in multiple languages ensures this use case of the retrieval model.

 

Educational assistance

 

The application of a retrieval model in the educational arena is an extension of question answering systems. It uses the said system, particularly for educational queries of users. In answering questions and generating academic content, the system can create more comprehensive results with contextually relevant information.

 

 

Future of RAG

 

The integration of retrieval and generation models in LLM is expected to grow in the future. The current trends indicate their increasing use in technological applications. Some common areas of future development of RAG include:

 

• Improved architecture – the development of retrieval and generation models will result in the innovation of neural network architectures

 

• Enhanced conversational agents – improved adaptation of knowledge base into retrieval model databases will result in more sophisticated conversational agents that can adapt to domain-specific information in an improved manner

 

• Integration with multimodal information – including different types of information, including images and audio, can result in contextually rich responses that encompass a diverse range of media

 

• Increased focus on ethical concerns – since data privacy and ethics are becoming increasingly important in today’s digital world, the retrieval models will also focus more on mitigating biases and ethical concerns from the development systems

 

 

Hence, retrieval augmented generation is an important aspect of large language models within the arena of generative AI. It has improved the overall content processing and promises an improved architecture of LLMs in the future.

Traditional databases in healthcare struggle to grasp the complex relationships between patients and their clinical histories. This limitation hinders personalized medicine and hampers rapid diagnosis. Vector databases, with their ability to store and query high-dimensional patient data, emerge as a revolutionary solution.

This blog delves into the technical details of how AI in healthcare empowers patient similarity searches and paves the path for precision medicine.

Impact of AI on healthcare

The healthcare landscape is brimming with data such as demographics, medical records, lab results, imaging scans, – the list goes on. While these large datasets hold immense potential for personalized medicine and groundbreaking discoveries, traditional relational databases cannot store such high-dimensional data at a large scale and often fall short.

Their rigid structure struggles to represent the intricate connections and nuances inherent in patient data.

Vector databases are revolutionizing healthcare data management. Unlike traditional, table-like structures, they excel at handling the intricate, multi-dimensional nature of patient information.

Each patient becomes a unique point in a high-dimensional space, defined by their genetic markers, lab values, and medical history. This dense representation unlocks powerful capabilities discussed later.

Working with vector data is tough because regular databases, which usually handle one piece of information at a time, can’t handle the complexity and large amount of this type of data. This makes it hard to find important information and analyze it quickly.

That’s where vector databases come in handy—they are made on purpose to handle this special kind of data. They give you the speed, ability to grow, and flexibility you need to get the most out of your data.

 

 

Patient similarity search with vector databases in healthcare

The magic lies in the ability to perform a similarity search. By calculating the distance between patient vectors, we can identify individuals with similar clinical profiles. This opens a large span of possibilities.

Personalized treatment plans

By uncovering patients with comparable profiles and treatment outcomes, doctors can tailor interventions with greater confidence and optimize individual care. It also serves as handy for medical researchers to look for efficient cures or preventions for a disease diagnosed over multiple patients by analyzing their data, particularly for a certain period. 

Here’s how vector databases transform treatment plans:

• Precise Targeting: By comparing a patient’s vector to those of others who have responded well to specific treatments, doctors can identify the most promising options with laser-like accuracy. This reduces the guesswork and minimizes the risk of ineffective therapies.
• Predictive Insights: Vector databases enable researchers to analyze the trajectories of similar patients, predicting their potential responses to different treatments. This foresight empowers doctors to tailor interventions, preventing complications and optimizing outcomes proactively.
• Unlocking Untapped Potential: By uncovering hidden connections between seemingly disparate data points, vector databases can reveal new therapeutic targets and treatment possibilities. This opens doors for personalized medicine breakthroughs that were previously unimaginable.
• Dynamic Adaptation: As a patient’s health evolves, their vector map shifts and readjusts accordingly. This allows for real-time monitoring and continuous refinement of treatment plans, ensuring the best possible care at every stage of the journey.

 

 

Drug discovery and repurposing

Identifying patients similar to those successfully treated with a specific drug can accelerate clinical trials and uncover unexpected connections for existing medications.

• Accelerated exploration: They transform complex drug and disease data into dense vectors, allowing for rapid similarity searches and the identification of promising drug candidates. Imagine sifting through millions of molecules at a single glance, pinpointing those with similar properties to known effective drugs.
• Repurposing potential: Vector databases can unearth hidden connections between existing drugs and potential new applications. By comparing drug vectors to disease vectors, they can reveal unexpected repurposing opportunities, offering a faster and cheaper path to new treatments. 
• Personalization insights: By weaving genetic and patient data into the drug discovery tapestry, vector databases can inform the development of personalized medications tailored to individual needs and responses. This opens the door to a future where treatments are as unique as the patients themselves. 
• Predictive power: Analyzing the molecular dance within the vector space can unveil potential side effects and predict drug efficacy before entering clinical trials. This helps navigate the treacherous waters of development, saving time and resources while prioritizing promising candidates. 

Cohort analysis in research

Grouping patients with similar characteristics facilitates targeted research efforts, leading to faster breakthroughs in disease understanding and treatment development.

• Exploring Disease Mechanisms: Vector databases facilitate the identification of patient clusters that share similar disease progression patterns. This can shed light on underlying disease mechanisms and guide the development of novel diagnostic markers and therapeutic target 
• Unveiling Hidden Patterns: Vector databases excel at similarity search, enabling researchers to pinpoint patients with similar clinical trajectories, even if they don’t share the same diagnosis or traditional risk factors. This reveals hidden patterns that might have been overlooked in traditional data analysis methods.

 

 

Technicalities of vector databases

Using a vector database enables the incorporation of advanced functionalities into our artificial intelligence, such as semantic information retrieval and long-term memory. The diagram provided below enhances our comprehension of the significance of vector databases in such applications.

 

 

Let’s break down the illustrated process:

• Initially, we employ the embedding model to generate vector embeddings for the content intended for indexing.
• The resulting vector embedding is then placed into the vector database, referencing the original content from which the embedding was derived. 
• Upon receiving a query from the application, we utilize the same embedding model to create embeddings for the query. These query embeddings are subsequently used to search the database for similar vector embeddings. As previously noted, these analogous embeddings are linked to the initial content from which they were created.

In comparison to the working of a traditional database, where data is stored as common data types like string, integer, date, etc. Users query the data by comparison with each row; the result of this query is the rows where the condition of the query is withheld.

In vector databases, this process of querying is more optimized and efficient with the use of a similarity metric for searching the most similar vector to our query. The search involves a combination of various algorithms, like approximate nearest neighbor optimization, which uses hashing, quantization, and graph-based detection.

Here are a few key components of the discussed process described below:

• Feature engineering: Transforming raw clinical data into meaningful numerical representations suitable for vector space. This may involve techniques like natural language processing for medical records or dimensionality reduction for complex biomolecular data. 
• Distance metrics: Choosing the appropriate distance metric to calculate the similarity between patient vectors. Popular options include Euclidean distance, cosine similarity, and Manhattan distance, each capturing different aspects of the data relationships.

 

 

• • Cosine Similarity: Calculates the cosine of the angle between two vectors in a vector space. It varies from -1 to 1, with 1 indicating identical vectors, 0 denoting orthogonal vectors, and -1 representing diametrically opposed vectors.

• • Euclidean Distance: Measures the straight-line distance between two vectors in a vector space. It ranges from 0 to infinity, where 0 signifies identical vectors and larger values indicate increasing dissimilarity between vectors.

• • Dot Product: Evaluate the product of the magnitudes of two vectors and the cosine of the angle between them. Its range is from -∞ to ∞, with a positive value indicating vectors pointing in the same direction, 0 representing orthogonal vectors, and a negative value signifying vectors pointing in opposite directions. 

• Nearest neighbor search algorithms: Efficiently retrieving the closest patient vectors to a given query. Techniques like k-nearest neighbors (kNN) and Annoy trees excel in this area, enabling rapid identification of similar patients.

 

A general pipeline from storing vectors to querying them is shown in the figure below:

 

 

• Indexing: The vector database utilizes algorithms like PQ, LSH, or HNSW (detailed below) to index vectors. This process involves mapping vectors to a data structure that enhances search speed. 
• Querying: The vector database examines the indexed query vector against the dataset’s indexed vectors, identifying the nearest neighbors based on a similarity metric employed by that specific index. 
• Post Processing: In certain instances, the vector database retrieves the ultimate nearest neighbors from the dataset and undergoes post-processing to deliver the final results. This step may involve re-evaluating the nearest neighbors using an alternative similarity measure.

Challenges and considerations

While vector databases offer immense potential, challenges remain:

• Data privacy and security: Safeguarding patient data while harnessing its potential for enhanced healthcare outcomes requires the implementation of robust security protocols and careful consideration of ethical standards.

This involves establishing comprehensive measures to protect sensitive information, ensuring secure storage, and implementing stringent access controls.

Additionally, ethical considerations play a pivotal role, emphasizing the importance of transparent data handling practices, informed consent procedures, and adherence to privacy regulations. As healthcare organizations leverage the power of data to advance patient care, a meticulous approach to security and ethics becomes paramount to fostering trust and upholding the integrity of the healthcare ecosystem. 

• Explainability and interpretability: Gaining insight into the reasons behind patient similarity is essential for informed clinical decision-making. It is crucial to develop transparent models that not only analyze the “why” behind these similarities but also offer insights into the importance of features within the vector space.

This transparency ensures a comprehensive understanding of the factors influencing patient similarities, contributing to more effective and reasoned clinical decisions. Integration with existing infrastructure: Seamless integration with legacy healthcare systems is essential for the practical adoption of vector database technology.

 

 

Revolution of medicine – AI in healthcare

In summary, the integration of vector databases in healthcare is revolutionizing patient care and diagnostics. Overcoming the limitations of traditional systems, these databases enable efficient handling of complex patient data, leading to precise treatment plans, accelerated drug discovery, and enhanced research capabilities.

While the technical aspects showcase the sophistication of these systems, challenges such as data privacy and seamless integration with existing infrastructure need attention. Despite these hurdles, the potential benefits promise a significant impact on personalized medicine and improved healthcare outcomes.

Large language models (LLMs) are a fascinating aspect of machine learning.

Regarding selective prediction in large language models, it refers to the model’s ability to generate specific predictions or responses based on the given input.

This means that the model can focus on certain aspects of the input text to make more relevant or context-specific predictions. For example, if asked a question, the model will selectively predict an answer relevant to that question, ignoring unrelated information.

 

They function by employing deep learning techniques and analyzing vast datasets of text. Here’s a simple breakdown of how they work:

1. Architecture: LLMs use a transformer architecture, which is highly effective in handling sequential data like language. This architecture allows the model to consider the context of each word in a sentence, enabling more accurate predictions and the generation of text.
2. Training: They are trained on enormous amounts of text data. During this process, the model learns patterns, structures, and nuances of human language. This training involves predicting the next word in a sentence or filling in missing words, thereby understanding language syntax and semantics.
3. Capabilities: Once trained, LLMs can perform a variety of tasks such as translation, summarization, question answering, and content generation. They can understand and generate text in a way that is remarkably similar to human language.

 

 

How selective predictions work in LLMs

Selective prediction in the context of large language models (LLMs) is a technique aimed at enhancing the reliability and accuracy of the model’s outputs. Here’s how it works in detail:

1. Decision to Predict or Abstain: At its core, selective prediction involves the model making a choice about whether to make a prediction or to abstain from doing so. This decision is based on the model’s confidence in its ability to provide a correct or relevant answer.
2. Improving Reliability: By allowing LLMs to abstain from making predictions in cases where they are unsure, selective prediction improves the overall reliability of the model. This is crucial in applications where providing incorrect information can have serious consequences.
3. Self-Evaluation: Some selective prediction techniques involve self-evaluation mechanisms. These allow the model to assess its own predictions and decide whether they are likely to be accurate or not. For example, experiments with models like PaLM-2 and GPT-3 have shown that self-evaluation-based scores can improve accuracy and correlation with correct answers.
4. Advanced Techniques like ASPIRE: Google’s ASPIRE framework is an example of an advanced approach to selective prediction. It enhances the ability of LLMs to make more confident predictions by effectively assessing when to predict and when to withhold a response.
5. Selective Prediction in Applications: This technique can be particularly useful in applications like conformal prediction, multi-choice question answering, and filtering out low-quality predictions. It ensures that the model provides responses only when it has a high degree of confidence, thereby reducing the risk of disseminating incorrect information.

 

 

Here’s how it works and improves response quality:

Example:

Imagine using a language model for a task like answering trivia questions. The LLM is prompted with a question: “What is the capital of France?” Normally, the model would generate a response based on its training.

However, with selective prediction, the model first evaluates its confidence in its knowledge about the answer. If it’s highly confident (knowing that Paris is the capital), it proceeds with the response. If not, it may abstain from answering or express uncertainty rather than providing a potentially incorrect answer.

 

 

Improvement in response quality:

1. Reduces Misinformation: By abstaining from answering when uncertain, selective prediction minimizes the risk of spreading incorrect information.
2. Enhances Reliability: It improves the overall reliability of the model by ensuring that responses are given only when the model has high confidence in their accuracy.
3. Better User Trust: Users can trust the model more, knowing that it avoids guessing when unsure, leading to higher quality and more dependable interactions.

Selective prediction, therefore, plays a vital role in enhancing the quality and reliability of responses in real-world applications of LLMs.

 

ASPIRE framework for selective predictions

The ASPIRE framework, particularly in the context of selective prediction for Large Language Models (LLMs), is a sophisticated process designed to enhance the model’s prediction capabilities. It comprises three main stages:

1. Task-Specific Tuning: In this initial stage, the LLM is fine-tuned for specific tasks. This means adjusting the model’s parameters and training it on data relevant to the tasks it will perform. This step ensures that the model is well-prepared and specialized for the type of predictions it will make.
2. Answer Sampling: After tuning, the LLM engages in answer sampling. Here, the model generates multiple potential answers or responses to a given input. This process allows the model to explore a range of possible predictions rather than settle on the first plausible option.
3. Self-Evaluation Learning: The final stage involves self-evaluation learning. The model evaluates the generated answers from the previous stage, assessing their quality and relevance. It learns to identify which answers are most likely to be correct or useful based on its training and the specific context of the question or task.

 

 

 

 

Helping businesses with informed decision-making

Businesses and industries can greatly benefit from adopting selective prediction frameworks like ASPIRE in several ways:

1. Enhanced Decision Making: By using selective prediction, businesses can make more informed decisions. The framework’s focus on task-specific tuning and self-evaluation allows for more accurate predictions, which is crucial in strategic planning and market analysis.
2. Risk Management: Selective prediction helps in identifying and mitigating risks. By accurately predicting market trends and customer behavior, businesses can proactively address potential challenges.
3. Efficiency in Operations: In industries such as manufacturing, selective prediction can optimize supply chain management and production processes. This leads to reduced waste and increased efficiency.
4. Improved Customer Experience: In service-oriented sectors, predictive frameworks can enhance customer experience by personalizing services and anticipating customer needs more accurately.
5. Innovation and Competitiveness: Selective prediction aids in fostering innovation by identifying new market opportunities and trends. This helps businesses stay competitive in their respective industries.
6. Cost Reduction: By making more accurate predictions, businesses can reduce costs associated with trial and error and inefficient processes.

 

Learn more about how DALLE, GPT 3, and MuseNet are reshaping industries.

 

Enhance trust with LLMs

Selective prediction frameworks like ASPIRE offer businesses and industries a strategic advantage by enhancing decision-making, improving operational efficiency, managing risks, fostering innovation, and ultimately leading to cost savings.

Overall, the ASPIRE framework is designed to refine the predictive capabilities of LLMs, making them more accurate and reliable by focusing on task-specific tuning, exploratory answer generation, and self-assessment of generated responses

In summary, selective prediction in LLMs is about the model’s ability to judge its own certainty and decide when to provide a response. This enhances the trustworthiness and applicability of LLMs in various domains.

Mistral AI, a startup co-founded by individuals with experience at Google’s DeepMind and Meta, made a significant entrance into the world of LLMs with Mistral 7B.

This model can be easily accessed and downloaded from GitHub or via a 13.4-gigabyte torrent, emphasizing accessibility. Mistral 7b, a 7.3 billion parameter model with the sheer size of some of its competitors, Mistral 7b punches well above its weight in terms of capability and efficiency. 

What makes Mistral 7b a great competitor? 

One of the key strengths of Mistral 7b lies in its architecture. Unlike many LLMs relying solely on transformer networks, Mistral 7b incorporates a hybrid approach, leveraging transformers and recurrent neural networks (RNNs). This unique blend allows Mistral 7b to excel at tasks that require both long-term memory and context awareness, such as question answering and code generation. 

Furthermore, Mistral 7b utilizes innovative attention mechanisms like group query attention and sliding window attention. These techniques enable the model to focus on relevant parts of the input data more effectively, improving performance and efficiency. 

 

Learn in detail about llm evaluation method

 

Mistral 7b architecture 

Mistral 7B is an architecture based on transformer architecture and introduces several innovative features and parameters. Here’s a gist of the architectural details: 

 

1. Sliding window attention: 

Mistral 7B addresses the quadratic complexity of vanilla attention by implementing Sliding Window Attention (SWA). 

SWA allows each token to attend to a maximum of W tokens from the previous layer (here, W = 3). 

Tokens outside the sliding window still influence next-word prediction. 

Information can propagate forward by up to k × W tokens after k attention layers. 

Parameters include dim = 4096, n_layers = 32, head_dim = 128, hidden_dim = 14336, n_heads = 32, n_kv_heads = 8, window_size = 4096, context_len = 8192, and vocab_size = 32000. 

 

 

Source:E2Enetwork 

 

 

2. Rolling Buffer Cache: 

This fixed-size cache serves as the “memory” for the sliding window attention. It efficiently stores key-value pairs for recent timesteps, eliminating the need for recomputing that information. A set attention span stays constant, managed by a rolling buffer cache limiting its size. 

Within the cache, each time step’s keys and values are stored at a specific location, determined by i mod W, where W is the fixed cache size. When the position i exceeds W, previous values in the cache get replaced. 

This method slashes cache memory usage by 8 times while maintaining the model’s effectiveness. 

 

 

Source:E2Enetwork 

 

 

3. Pre-fill and chunking: 

During sequence generation, the cache is pre-filled with the provided prompt to enhance context. For long prompts, chunking divides them into smaller segments, each treated with both cache and current chunk attention, further optimizing the process.

When creating a sequence, tokens are guessed step by step, with each token relying on the ones that came before it. The starting information, known as the prompt, lets us fill the (key, value) cache beforehand with this prompt.

The chunk size can determine the window size, and the attention mask is used across both the cache and the chunk. This ensures the model gets the necessary information while staying efficient. 

 

Source:E2Enetwork 

 

 

Comparison of performance: Mistral 7B vs Llama2-13B  

The true test of any LLM lies in its performance on real-world tasks. Mistral 7b has been benchmarked against several established models, including Llama 2 (13B parameters) and Llama 1 (34B parameters).

The results are impressive, with Mistral 7b outperforming both models on all tasks tested. It even approaches the performance of CodeLlama 7B (also 7B parameters) on code-related tasks while maintaining strong performance on general language tasks. Performance comparisons were conducted across a wide range of benchmarks, encompassing various aspects.

 

 

1. Performance comparison 

Mistral 7B surpasses Llama2-13B across various benchmarks, excelling in commonsense reasoning, world knowledge, reading comprehension, and mathematical tasks. Its dominance isn’t marginal; it’s a robust demonstration of its capabilities. 

 

2. Equivalent Model Capacity 

In reasoning, comprehension, and STEM tasks, Mistral 7B functions akin to a Llama2 model over three times its size. This not only highlights its efficiency in memory usage but also its enhanced processing speed. Essentially, it offers immense power within an elegantly streamlined design. 

 

3. Knowledge-based assessments 

Mistral 7B demonstrates superiority in most assessments and competes equally with Llama2-13B in knowledge-based benchmarks. This parallel performance in knowledge tasks is especially intriguing, given Mistral 7B’s comparatively restrained parameter count. 

 

 

Source:MistralAI 

 

Beyond benchmarks: Practical applications 

The capabilities of Mistral 7b extend far beyond benchmark scores Mistral 7B isn’t limited to a single skill. It performs exceptionally well across various tasks, spanning code-related fields and English language tasks. Remarkably, it matches CodeLlama-7B’s performance in coding tasks, highlighting its adaptability and wide-ranging abilities.  Some of the common works in each field are mentioned below: 

• Natural Language Processing (NLP): Machine translation, text summarization, question answering, and sentiment analysis. 
• Code Generation and Analysis: Generate code snippets, translate natural language to code, and analyze existing code for potential issues. 
• Creative Writing: Compose poems, scripts, musical pieces, and other creative text formats. 
• Education and Research: Assist with research tasks, generate educational materials, and personalize learning experiences. 

 

 

 

Source:E2Enetwork 

 

Source:MistralAI 

 

A cost-effective Solution 

One of the most compelling aspects of Mistral 7b is its cost-effectiveness. Compared to models of similar size, Mistral 7b requires significantly less computational resources to run. This makes it a more accessible option for individuals and organizations with limited budgets. Additionally, Mistral AI offers flexible deployment options, allowing users to run the model on their own infrastructure or through the cloud. 

 

Versatile deployment 

Mistral 7B stands out due to its Apache 2.0 license, granting broad accessibility for diverse users, including individuals, major corporations, and governmental bodies.

This open-source license not only ensures inclusivity but also permits customization and adaptation to suit specific needs. It empowers users to modify, share, and utilize Mistral 7B for a wide array of applications, fostering innovation and collaboration in the community. 

 

The decentralization issue vs transparency 

Mistral AI prioritizes transparency and open access, yet safety concerns arise due to the fully decentralized ‘Mistral-7B-v0.1’ model, capable of unmoderated response generation.

Unlike models such as GPT and Llama, it lacks mechanisms to discern appropriate responses, posing potential exploitation risks. However, despite safety concerns, decentralized Language Model Models (LLMs) offer advantages, democratizing AI access and enabling positive applications. 

 

Are large language models the zero shot reasoners? Read more here

 

Conclusion 

Mistral 7b is a testament to the power of innovation in the LLM domain. Despite its relatively small size, it has established itself as a force to be reckoned with, delivering impressive performance across a wide range of tasks. With its focus on efficiency and cost-effectiveness, Mistral 7b is poised to democratize access to cutting-edge language technology and shape the future of how we interact with machines. 

 

The emergence of Large language models such as GPT-4 has been a transformative development in AI. These models have significantly advanced capabilities across various sectors, most notably in areas like content creation, code generation, and language translation, marking a new era in AI’s practical applications.

However, the deployment of these models is not without its challenges. LLMs demand extensive computational resources, consume a considerable amount of energy, and require substantial memory capacity.

These requirements can render LLMs impractical for certain applications, especially those with limited processing power or in environments where energy efficiency is a priority.

In response to these limitations, there has been a growing interest in the development of small language models (SLMs). These models are designed to be more compact and efficient, addressing the need for AI solutions that are viable in resource-constrained environments.

Let’s explore these models in greater detail and the rationale behind them.

What are small language models?

Small Language Models (SLMs) represent an intriguing segment of AI. Unlike their larger counterparts, GPT-4 and LlaMa 2, which boast billions, and sometimes trillions of parameters, SLMs operate on a much smaller scale, typically encompassing thousands to a few million parameters.

This relatively modest size translates into lower computational demands, making lesser-sized language models accessible and feasible for organizations or researchers who might not have the resources to handle the more substantial computational load required by larger models. Read more

 

 

However, since the race behind AI has taken its pace, companies have been engaged in a cut-throat competition of who’s going to make the bigger language model. Because bigger language models translated to be the better language models.

Given this, how do SLMs fit into this equation, let alone outperform large language models?

How can small language models function well with fewer parameters?

 

There are several reasons why lesser-sized language models fit into the equation of language models.

The answer lies in the training methods. Different techniques like transfer learning allow smaller models to leverage pre-existing knowledge, making them more adaptable and efficient for specific tasks. For instance, distilling knowledge from LLMs into SLMs can result in models that perform similarly but require a fraction of the computational resources.

Secondly, compact models can be more domain-specific. By training them on specific datasets, these models can be tailored to handle specific tasks or cater to particular industries, making them more effective in certain scenarios.

For example, a healthcare-specific SLM might outperform a general-purpose LLM in understanding medical terminology and making accurate diagnoses.

Despite these advantages, it’s essential to remember that the effectiveness of an SLM largely depends on its training and fine-tuning process, as well as the specific task it’s designed to handle. Thus, while lesser-sized language models can outperform LLMs in certain scenarios, they may not always be the best choice for every application.

Collaborative advancements in small language models

 

Hugging Face, along with other organizations, is playing a pivotal role in advancing the development and deployment of SLMs. The company has created a platform known as Transformers, which offers a range of pre-trained SLMs and tools for fine-tuning and deploying these models. This platform serves as a hub for researchers and developers, enabling collaboration and knowledge sharing. It expedites the advancement of lesser-sized language models by providing necessary tools and resources, thereby fostering innovation in this field.

Similarly, Google has contributed to the progress of lesser-sized language models by creating TensorFlow, a platform that provides extensive resources and tools for the development and deployment of these models. Both Hugging Face’s Transformers and Google’s TensorFlow facilitate the ongoing improvements in SLMs, thereby catalyzing their adoption and versatility in various applications.

Moreover, smaller teams and independent developers are also contributing to the progress of lesser-sized language models. For example, “TinyLlama” is a small, efficient open-source language model developed by a team of developers, and despite its size, it outperforms similar models in various tasks. The model’s code and checkpoints are available on GitHub, enabling the wider AI community to learn from, improve upon, and incorporate this model into their projects.

These collaborative efforts within the AI community not only enhance the effectiveness of SLMs but also greatly contribute to the overall progress in the field of AI.

Phi-2: Microsoft’s small language model with 2.7 billion parameters

What are the potential implications of SLMs in our personal lives?

Small Language Models have the potential to significantly enhance various facets of our personal lives, from smartphones to home automation. Here’s an expanded look at the areas where they could be integrated:

 

1.       Smartphones:

SLMs are well-suited for the limited hardware of smartphones, supporting on-device processing that quickens response times, enhances privacy and security, and aligns with the trend of edge computing in mobile technology.

This integration paves the way for advanced personal assistants capable of understanding complex tasks and providing personalized interactions based on user habits and preferences.

Additionally, SLMs in smartphones could lead to more sophisticated, cloud-independent applications, improved energy efficiency, and enhanced data privacy.

They also hold the potential to make technology more accessible, particularly for individuals with disabilities, through features like real-time language translation and improved voice recognition.

The deployment of lesser-sized language models in mobile technology could significantly impact various industries, leading to more intuitive, efficient, and user-focused applications and services.

2.       Smart Home Devices:

 

Voice-Activated Controls: SLMs can be embedded in smart home devices like thermostats, lights, and security systems for voice-activated control, making home automation more intuitive and user-friendly.

Personalized Settings: They can learn individual preferences for things like temperature and lighting, adjusting settings automatically for different times of day or specific occasions.

3.       Wearable Technology:

 

Health Monitoring: In devices like smartwatches or fitness trackers, lesser-sized language models can provide personalized health tips and reminders based on the user’s activity levels, sleep patterns, and health data.

Real-Time Translation: Wearables equipped with SLMs could offer real-time translation services, making international travel and communication more accessible.

4.       Automotive Systems:

 

Enhanced Navigation and Assistance: In cars, lesser-sized language models can offer advanced navigation assistance, integrating real-time traffic updates, and suggesting optimal routes.

Voice Commands: They can enhance the functionality of in-car voice command systems, allowing drivers to control music, make calls, or send messages without taking their hands off the wheel.

5.       Educational Tools:

 

Personalized Learning: Educational apps powered by SLMs can adapt to individual learning styles and paces, providing personalized guidance and support to students.

Language Learning: They can be particularly effective in language learning applications, offering interactive and conversational practice.

6.       Entertainment Systems:

 

Smart TVs and Gaming Consoles: SLMs can be used in smart TVs and gaming consoles for voice-controlled operation and personalized content recommendations based on viewing or gaming history.

The integration of lesser-sized language models across these domains, including smartphones, promises not only convenience and efficiency but also a more personalized and accessible experience in our daily interactions with technology. As these models continue to evolve, their potential applications in enhancing personal life are vast and ever-growing.

Do SLMs pose any challenges?

Small Language Models do present several challenges despite their promising capabilities

1. Limited Context Comprehension: Due to the lower number of parameters, SLMs may have less accurate and nuanced responses compared to larger models, especially in complex or ambiguous situations.
2. Need for Specific Training Data: The effectiveness of these models heavily relies on the quality and relevance of their training data. Optimizing these models for specific tasks or applications requires expertise and can be complex.
3. Local CPU Implementation Challenges: Running a compact language model on local CPUs involves considerations like optimizing memory usage and scaling options. Regular saving of checkpoints during training is necessary to prevent data loss.
4. Understanding Model Limitations: Predicting the performance and potential applications of lesser-sized language models can be challenging, especially in extrapolating findings from smaller models to their larger counterparts.

Embracing the future with small language models

The journey through the landscape of SLMs underscores a pivotal shift in the field of artificial intelligence. As we have explored, lesser-sized language models emerge as a critical innovation, addressing the need for more tailored, efficient, and sustainable AI solutions. Their ability to provide domain-specific expertise, coupled with reduced computational demands, opens up new frontiers in various industries, from healthcare and finance to transportation and customer service.

The rise of platforms like Hugging Face’s Transformers and Google’s TensorFlow has democratized access to these powerful tools, enabling even smaller teams and independent developers to make significant contributions. The case of “Tiny Llama” exemplifies how a compact, open-source language model can punch above its weight, challenging the notion that bigger always means better.

As the AI community continues to collaborate and innovate, the future of lesser-sized language models is bright and promising. Their versatility and adaptability make them well-suited to a world where efficiency and specificity are increasingly valued. However, it’s crucial to navigate their limitations wisely, acknowledging the challenges in training, deployment, and context comprehension.

In conclusion, compact language models stand not just as a testament to human ingenuity in AI development but also as a beacon guiding us toward a more efficient, specialized, and sustainable future in artificial intelligence.

Large Language Models (LLMs) like GPT-3 and BERT have revolutionized the field of natural language processing. However, large language models evaluation is as crucial as their development. This blog delves into the methods used to assess LLMs, ensuring they perform effectively and ethically.

 

 

 

Evaluation metrics and methods

1. Perplexity: Perplexity measures how well a model predicts a text sample. A lower perplexity indicates better performance, as the model is less ‘perplexed’ by the data.
2. Accuracy, safety, and fairness: Beyond mere performance, assessing an LLM involves evaluating its accuracy in understanding and generating language, safety in avoiding harmful outputs, and fairness in treating all groups equitably.
3. Embedding-based methods: Methods like BERTScore use embeddings (vector representations of text) to evaluate semantic similarity between the model’s output and reference texts.
4. Human evaluation panels: Panels of human evaluators can judge the model’s output for aspects like coherence, relevance, and fluency, offering insights that automated metrics might miss.
5. Benchmarks like MMLU and HellaSwag: These benchmarks test an LLM’s ability to handle complex language tasks and scenarios, gauging its generalizability and robustness.
6. Holistic evaluation: Frameworks like the Holistic Evaluation of Language Models (HELM) assess models across multiple metrics, including accuracy and calibration, to provide a comprehensive view of their capabilities.
7. Bias detection and interpretability methods: These methods evaluate how biased a model’s outputs are and how interpretable its decision-making process is, addressing ethical considerations.

 

 

 

Have you heard about Microsoft’s latest tech marvel in the AI world? It’s called Phi-2, a nifty little language model that’s stirring up quite the excitement.

Despite its compact size of 2.7 billion parameters, this little dynamo is an upgrade from its predecessor, Phi-1.5. What’s cool is that it’s all set and ready for you to explore in the Azure AI Studio model catalogue.

 

Now, Phi-2 isn’t just any small language model. Microsoft’s team, led by Satya Nadella, showcased it at Ignite 2023, and guess what? They say it’s a real powerhouse, even giving the bigger players like Llama-2 and Gemini-2 a run for their money in generative AI tests.

This model isn’t just about crunching data; it’s about understanding language, making sense of the world, and reasoning logically. Microsoft even claims it can outdo models 25 times their size in certain tasks.

 

Read in detail about: Google launches Gemini AI

 

But here’s the kicker: training Phi-2 is a breeze compared to giants like GPT-4. It gets its smarts from a mix of high-quality data, including synthetic sets, everyday knowledge, and more. It’s built on a transformer framework, aiming to predict the next word in a sequence. And the training? Just 14 days on 96 A100 GPUs. Now, that’s efficient, especially when you think about GPT-4 needing up to 100 days and a whole lot more GPUs!

 

 

Comparative analysis of Phi-2

Comparing Phi 2, Llama 2, and other notable language models can provide insights into their unique strengths and applications.

1. Phi 2 (Microsoft):
   • Size and Architecture: A smaller model with 2.7 billion parameters, utilizing a transformer-based architecture for efficient next-word prediction.
   • Training and Data: Trained on 1.4 trillion tokens, Phi 2 is designed for common-sense reasoning and language understanding.
   • Application: Its smaller size makes it suitable for research and development in language models, emphasizing reasoning and understanding.
2. Llama 2 (Meta AI):
   • Training and Scope: Llama 2 is a code generation model built on a base of 500 billion tokens of code, indicating a focus on programming languages and coding applications.
   • Capabilities: It supports common programming languages and is optimized for dialogue use cases.
   • Usage: Geared towards generating code and supporting various programming languages, it is ideal for software development and related fields.
3. Other Language Models (General Overview):
   • Models like BERT, GPT-3, Bloom, and WuDao 2.0 vary in size, training data, and applications. They range from few billion to hundreds of billions of parameters.
   • These models are used in diverse applications, including natural language processing, chatbot development, content creation, and more.
   • Each model has its own unique strengths and limitations, with some focusing on specific languages, tasks, or scales of operation.

 

 

Phi-2 features and capabilities

Phi-2 is a new language model developed by Microsoft, marking a significant advancement in AI technology. It stands out for several key features and capabilities:

1. Transformer-Based Model: Phi-2 utilizes a transformer-based architecture, focusing on next-word prediction, which is a common approach in modern language models.
2. Training Data and Size: This model is trained on 1.4 trillion tokens, indicating a substantial dataset for its learning process. Despite this, Phi-2 is referred to as a “small” language model, with 2.7 billion parameters, which is relatively small compared to some other language models in the field.
3. Capabilities: Phi-2 demonstrates impressive capabilities in common-sense reasoning and language understanding. This makes it adept at handling various linguistic tasks and reasoning challenges.
4. Comparative Performance: The model reportedly outperforms other models like the Llama 2 and Mistral 7B, indicating its efficiency and robustness despite its smaller size.
5. Purpose and Application: Phi-2 is geared towards research and development in the field of language models, reflecting Microsoft’s ongoing efforts to advance AI technology.

 

Read in detail about: Multimodality revolution

 

In summary, while Phi 2 and Llama 2 are both advanced language models, they serve different purposes. Phi 2 excels in language understanding and reasoning, making it suitable for research and development, while Llama 2 focuses on code generation and software development applications. Other models, like GPT-3 or BERT, have broader applications and are often used in content generation and natural language understanding tasks.

 Large language models (LLMs), such as OpenAI’s GPT-4, are swiftly metamorphosing from mere text generators into autonomous, goal-oriented entities displaying intricate reasoning abilities. This crucial shift carries the potential to revolutionize the manner in which humans connect with AI, ushering us into a new frontier.

This blog will break down the working of these agents, illustrating the impact they impart on what is known as the ‘Lang Chain’. 

 

Working of the agents 

Our exploration into the realm of LLM agents begins with understanding the key elements of their structure, namely the LLM core, the Prompt Recipe, the Interface and Interaction, and Memory. The LLM core forms the fundamental scaffold of an LLM agent. It is a neural network trained on a large dataset, serving as the primary source of the agent’s abilities in text comprehension and generation. 

The functionality of these agents heavily relies on prompt engineering. Prompt recipes are carefully crafted sets of instructions that shape the agent’s behaviors, knowledge, goals, and persona and embed them in prompts. 

 

 

 

The agent’s interaction with the outer world is dictated by its user interface, which could vary from command-line, graphical, to conversational interfaces. In the case of fully autonomous agents, prompts are programmatically received from other systems or agents.

Another crucial aspect of their structure is the inclusion of memory, which can be categorized into short-term and long-term. While the former helps the agent be aware of recent actions and conversation histories, the latter works in conjunction with an external database to recall information from the past. 

 

Learn in detail about LangChain

 

Ingredients involved in agent creation 

Creating robust and capable LLM agents demands integrating the core LLM with additional components for knowledge, memory, interfaces, and tools.

 

 

The LLM forms the foundation, while three key elements are required to allow these agents to understand instructions, demonstrate essential skills, and collaborate with humans: the underlying LLM architecture itself, effective prompt engineering, and the agent’s interface. 

 

Tools 

Tools are functions that an agent can invoke. There are two important design considerations around tools: 

• Giving the agent access to the right tools 
• Describing the tools in a way that is most helpful to the agent 

Without thinking through both, you won’t be able to build a working agent. If you don’t give the agent access to a correct set of tools, it will never be able to accomplish the objectives you give it. If you don’t describe the tools well, the agent won’t know how to use them properly. Some of the vital tools a working agent needs are:

 

1. SerpAPI : This page covers how to use the SerpAPI search APIs within Lang Chain. It is broken into two parts: installation and setup, and then references to the specific SerpAPI wrapper. Here are the details for its installation and setup:

• Install requirements with pip install google-search-results 
• Get a SerpAPI api key and either set it as an environment variable (SERPAPI_API_KEY) 

You can also easily load this wrapper as a tool (to use with an agent). You can do this with:

 

2. Math-tool: The llm-math tool wraps an LLM to do math operations. It can be loaded into the agent tools like: 

Python-REPL tool: Allows agents to execute Python code. To load this tool, you can use: 

 

 

 

 

The action of python REPL allows agent to execute the input code and provide the response. 

 

The impact of agents: 

A noteworthy advantage of LLM agents is their potential to exhibit self-initiated behaviors ranging from purely reactive to highly proactive. This can be harnessed to create versatile AI partners capable of comprehending natural language prompts and collaborating with human oversight. 

 

 

LLM agents leverage LLMs innate linguistic abilities to understand instructions, context, and goals, operate autonomously and semi-autonomously based on human prompts, and harness a suite of tools such as calculators, APIs, and search engines to complete assigned tasks, making logical connections to work towards conclusions and solutions to problems. Here are few of the services that are highly dominated by the use of Lang Chain agents:

 

 

 

Facilitating language services 

Agents play a critical role in delivering language services such as translation, interpretation, and linguistic analysis. Ultimately, this process steers the actions of the agent through the encoding of personas, instructions, and permissions within meticulously constructed prompts.

Users effectively steer the agent by offering interactive cues following the AI’s responses. Thoughtfully designed prompts facilitate a smooth collaboration between humans and AI. Their expertise ensures accurate and efficient communication across diverse languages. 

 

 

Quality assurance and validation 

Ensuring the accuracy and quality of language-related services is a core responsibility. Agents verify translations, validate linguistic data, and maintain high standards to meet user expectations. Agents can manage relatively self-contained workflows with human oversight.

Use internal validation to verify the accuracy and coherence of their generated content. Agents undergo rigorous testing against various datasets and scenarios. These tests validate the agent’s ability to comprehend queries, generate accurate responses, and handle diverse inputs. 

 

Types of agents 

Agents use an LLM to determine which actions to take and in what order. An action can either be using a tool and observing its output, or returning a response to the user. Here are the agents available in Lang Chain.  

Zero-Shot ReAct: This agent uses the ReAct framework to determine which tool to use based solely on the tool’s description. Any number of tools can be provided. This agent requires that a description is provided for each tool. Below is how we can set up this Agent: 

 

 

Let’s invoke this agent and check if it’s working in chain 

 

 

This will invoke the agent. 

Structured-Input ReAct: The structured tool chat agent is capable of using multi-input tools. Older agents are configured to specify an action input as a single string, but this agent can use a tool’s argument schema to create a structured action input. This is useful for more complex tool usage, like precisely navigating around a browser. Here is how one can setup the React agent:

 

 

The further necessary imports required are:

 

 

Setting up parameters:

 

Creating the agent:

 

 

Improving performance of an agent 

Enhancing the capabilities of agents in Large Language Models (LLMs) necessitates a multi-faceted approach. Firstly, it is essential to keep refining the art and science of prompt engineering, which is a key component in directing these systems securely and efficiently. As prompt engineering improves, so does the competencies of LLM agents, allowing them to venture into new spheres of AI assistance.

Secondly, integrating additional components can expand agents’ reasoning and expertise. These components include knowledge banks for updating domain-specific vocabularies, lookup tools for data gathering, and memory enhancement for retaining interactions.

Thus, increasing the autonomous capabilities of agents requires more than just improved prompts; they also need access to knowledge bases, memory, and reasoning tools.

Lastly, it is vital to maintain a clear iterative prompt cycle, which is key to facilitating natural conversations between users and LLM agents. Repeated cycling allows the LLM agent to converge on solutions, reveal deeper insights, and maintain topic focus within an ongoing conversation. 

 

Conclusion 

The advent of large language model agents marks a turning point in the AI domain. With increasing advances in the field, these agents are strengthening their footing as autonomous, proactive entities capable of reasoning and executing tasks effectively.

The application and impact of Large Language Model agents are vast and game-changing, from conversational chatbots to workflow automation. The potential challenges or obstacles include ensuring the consistency and relevance of the information the agent processes, and the caution with which personal or sensitive data should be treated. The promising future outlook of these agents is the potentially increased level of automated and efficient interaction humans can have with AI. 

Large language models (LLMs) have revolutionized the field of natural language processing (NLP), enabling machines to generate human-quality text, translate languages, and answer questions in an informative way. These advancements have opened up a world of possibilities for applications in various domains, from customer service to education.  

Want to build a custom llm application? Check out our in-person Large Language Model bootcamp. 

However, mastering LLMs requires a comprehensive understanding of their underlying principles, architectures, and training techniques. 

 

 

 

This 7-step guide will provide you with a structured approach to mastering LLMs: 

Step 1: Understand LLM basics 

Before diving into the complexities of LLMs, it’s crucial to establish a solid foundation in the fundamental concepts. This includes understanding the following: 

• Natural Language Processing (NLP): NLP is the field of computer science that deals with the interaction between computers and human language. It encompasses tasks like machine translation, text summarization, and sentiment analysis. 

 

Read more about attention mechanisms in natural language processing

 

• Deep Learning: LLMs are powered by deep learning, a subfield of machine learning that utilizes artificial neural networks to learn from data. Familiarize yourself with the concepts of neural networks, such as neurons, layers, and activation functions. 

• Transformer: The transformer architecture is a cornerstone of modern LLMs. Understand the components of the transformer architecture, including self-attention, encoder-decoder architecture, and positional encoding. 

 

 

Step 2: Explore LLM architectures 

LLMs come in various architectures, each with its strengths and limitations. Explore different LLM architectures, such as: 

• BERT (Bidirectional Encoder Representations from Transformers): BERT is a widely used LLM that excels in natural language understanding tasks, such as question answering and sentiment analysis. 

• GPT (Generative Pre-training Transformer): GPT is known for its ability to generate human-quality text, making it suitable for tasks like creative writing and chatbots. 

• XLNet (Generalized Autoregressive Pre-training for Language Understanding): XLNet is an extension of BERT that addresses some of its limitations, such as its bidirectional nature. 

 

 

Step 3: Pre-training LLMs 

Pre-training is a crucial step in the development of LLMs. It involves training the LLM on a massive dataset of text and code to learn general language patterns and representations. Explore different pre-training techniques, such as: 

• Masked Language Modeling (MLM): In MLM, random words are masked in the input text, and the LLM is tasked with predicting the missing words. 

• Next Sentence Prediction (NSP): In NSP, the LLM is given two sentences and asked to determine whether they are consecutive sentences from a text or not. 

• Contrastive Language-Image Pre-training (CLIP): CLIP involves training the LLM to match text descriptions with their corresponding images. 

 

Step 4: Fine-tuning LLMs 

Fine-tuning involves adapting a pre-trained LLM to a specific task or domain. This is done by training the LLM on a smaller dataset of task-specific data. Explore different fine-tuning techniques, such as:

• Task-specific loss functions: Define loss functions that align with the specific task, such as accuracy for classification tasks or BLEU score for translation tasks. 

• Data augmentation: Augment the task-specific dataset to improve the LLM’s generalization ability. 

• Early stopping: Implement early stopping to prevent overfitting and optimize the LLM’s performance. 

 

This talk below can help you get started with fine-tuning GPT 3.5 Turbo. 

 

 

 

Step 5: Alignment and post-training 

Alignment and post-training are essential steps to ensure that LLMs are aligned with human values and ethical considerations. This includes: 

• Bias mitigation: Identify and mitigate biases in the LLM’s training data and outputs. 

• Fairness evaluation: Evaluate the fairness of the LLM’s decisions and identify potential discriminatory patterns. 

• Explainability: Develop methods to explain the LLM’s reasoning and decision-making processes. 

 

Step 6: Evaluating LLMs 

Evaluating LLMs is crucial to assess their performance and identify areas for improvement. Explore different evaluation metrics, such as: 

• Accuracy: Measure the proportion of correct predictions for classification tasks. 

• Fluency: Assess the naturalness and coherence of the LLM’s generated text. 

• Relevance: Evaluate the relevance of the LLM’s outputs to the given prompts or questions. 

 

Read more about: Evaluating large language models

 

Step 7: Build LLM apps 

With a strong understanding of LLMs, you can start building applications that leverage their capabilities. Explore different application scenarios, such as:

• Chatbots: Develop chatbots that can engage in natural conversations with users. 

• Content creation: Utilize LLMs to generate creative content, such as poems, scripts, or musical pieces. 

• Machine translation: Build machine translation systems that can accurately translate languages. 

 

 

Start learning large language models

Mastering large language models (LLMs) is an ongoing journey that requires continuous learning and exploration. By following these seven steps, you can gain a comprehensive understanding of LLMs, their underlying principles, and the techniques involved in their development and application.  

As LLMs continue to evolve, stay informed about the latest advancements and contribute to the responsible and ethical development of these powerful tools. Here’s a list of YouTube channels that can help you stay updated in the world of large language models.

Multimodality refers to an AI model’s ability to understand, process, and generate multiple types of information, such as text, images, and potentially even sounds. It’s the capacity to interpret and interact with various data forms, where the model not only reads textual information but also comprehends visual or other types of data.  

 

How does multimodality increase the power of LLMs?

The significance of multimodality lies in its potential to greatly enhance the effectiveness and applications of AI models.  

Consider the human intellect and its capacity to comprehend the world and tackle unique challenges. This ability stems from processing diverse forms of information, including language, sight, and taste, among others.

If an individual lacks access to one of these sensory inputs from the outset, such as vision, their understanding of the real world is likely to be significantly impaired. 

 

 

 

Hence, multimodality in models, like GPT-4, allows them to develop intuition and understand complex relationships not just inside single modalities but across them, mimicking human-level cognizance to a higher degree.  

 

Read about: GPT 3.5 VS GPT 4

 

Here are a few examples where we see that GPT-4 Vision is capable of performing human-like tasks:

 

Example 1: GPT-4 Vision and understanding humor

 

  Source: OpenAI 

 

 

Example 2: GPT-4 Vision acing complex exams  

 

 

 

 

Why does vision help GPT-4 do better on tests? Well, think about it like this: you’d probably get more out of an exam if it’s written down for you to see, rather than just hearing it from someone, right?

It’s the same deal with a model like the GPT-4. Having that visual element just makes things a bit clearer and easier to work with. 

Hence, multimodal learning opens up newer opportunities, helps AI handle real-world data more efficiently, and brings us closer to developing AI models that act and think more like humans. 

 

 

How does the GPT-4 with Vision model combine text and image inputs to provide responses? 

 

GPT-4 with Vision combines natural language processing capabilities with computer vision. This means it can accept different forms of input, like text and images, and deliver outputs based on that mixture of information.

This model represents a significant advance in machine learning and natural language processing, as it bridges two traditionally separate fields: computer vision and natural language processing. 

Enabling models to understand different types of data enhances their performance and expands their application scope. For instance, in the real-world, they may be used for Visual Question Answering (VQA), wherein the model is given an image and a text query about the image, and it needs to provide a suitable answer. 

 

Use-cases of GPT-4 Vision 

 

GPT-4V can perform a variety of tasks, including data deciphering, multi-condition processing, text transcription from images, object detection, coding enhancement, design understanding, and more. Here are some mind-boggling use cases of GPT-4 Vision. Of course, as time progresses, its usability will keep increasing.

1. Data Deciphering and Visualization: GPT-4V is capable of processing infographics or charts and providing detailed breakdowns of the data presented. This means that complex visual data can be transformed into understandable insights, making it easier for users to comprehend complex information. Here’s an example:

 

Source: Datacamp 

 

Conversely, the technology demonstrates proficiency in interpreting the provided data and generating impactful visual representations. Here’s an example where GPT-4 successfully processed LATEX code to produce a Python plot.

This was achieved through interactive dialogue with the user. In this scenario, the model accurately extracted the necessary data and efficiently addressed all user queries. It adeptly reformatted the data and tailored the visualization to meet the specified requirements. 

 

Source: Sparks of Artificial General Intelligence: Early experiments with GPT-4 | Microsoft 

 

 

1. Multi-condition processing:

GPT-4V is excellent at analyzing images under varying conditions, such as different lighting or complex scenes, and can provide insightful details drawn from these varying contexts.  

 

Source: roboflow 

 

Text Transcription

The model is geared to transcribe text from images. It could be a game-changer in digitizing written or printed documents by converting images of text into a digital format. 

 

Object Detection

GPT-4V has superior object detection capabilities. It can accurately identify different objects within an image, even abstract ones, providing a comprehensive analysis and comprehension of images. 

 

 

Source: roboflow 

 

 

Game Development:

GPT-4V can significantly impact the gaming industry as well. Here an example where it was provided with a comprehensive overview of a 3D game. GPT-4 demonstrated its capability to develop a functional game using HTML and JavaScript. This is accomplished without prior training or experience in related projects. 

Source: Sparks of Artificial General Intelligence: Early experiments with GPT-4 | Microsoft 

 

 

Web Development:

GPT-4 Vision significantly enhances web development by enabling the creation of websites from visual inputs like sketches. It interprets design elements and transforms them into functional HTML, CSS, and JavaScript code, including interactive features and specific themes, such as a ’90s hacker style with dynamic effects. Here’s an example where GPT-4 was prompted to write code for a website by only providing it a hand drawn sketch:  

 

Source: Datacamp 

 

 

Once the HTML and CSS files were created as instructed, this was the result: 

 

Source: Datacamp 

 

This advancement streamlines the web development process, making it more accessible and efficient, particularly for those with limited coding knowledge. It opens up new possibilities for creative design and can be applied across various domains, potentially evolving with continuous learning and improvement. 

 

 

Complex Mathematical Analysis: GPT-4V can process and analyze intricate mathematical expressions, especially when they are represented graphically or in handwritten forms. 

 

 

Source: roboflow 

 

 

Integrations with Other Systems: GPT-4 can be integrated with other systems through its API, expanding its application sphere to diverse domains like security, healthcare diagnostics, and entertainment. 

Educational Assistance: GPT-4V can help in the educational sector by analysing diagrams, illustrations, and visual aids, and transforming them into detailed textual explanations, making concepts easier to comprehend for students and educators alike. 

The innovation of incorporating visual capabilities, therefore, offers a dynamic and engaging method for users to interact with AI systems. 

 

 

Where does GPT 4 Vision perform less effectively? 

While the GPT-4 Vision is groundbreaking, it is important to recognize its limitations and risks. 

• Privacy Concerns: GPT-4 Vision’s ability to identify individuals and locations in images raises serious privacy issues. This poses a challenge for companies to balance innovation with adherence to privacy laws and ethical practices. 
• Bias in Image Analysis: The risk of biases in image interpretation could lead to unfair or discriminatory outcomes, particularly affecting diverse demographic groups. This necessitates careful oversight and continuous improvement of the AI’s algorithms to minimize biases. 
• Unreliable Medical Advice or Dangerous Instructions: The model might inadvertently provide inaccurate medical advice or instructions for potentially hazardous tasks. This limitation is significant, especially in contexts where precise and reliable information is critical for safety and health. 
• Cybersecurity Vulnerabilities: GPT-4 Vision could be exploited for tasks like solving CAPTCHAs, posing cybersecurity risks. This highlights the need for robust security measures to prevent malicious use. 
• Content Accuracy and Hallucination: The model, like other AI systems, can sometimes generate content that is not factually correct or based in reality, known as ‘hallucinations’. Users must be vigilant and verify the information provided by the AI. 
• Refusal to Analyze Certain Images: In some cases, GPT-4 Vision might refuse to analyze images, particularly those involving people, due to the sensitive nature of such data. This limitation can be viewed as a measure to prevent misuse or ethical breaches, but it also restricts the model’s functionality in certain scenarios. 
• Overall, these risks and limitations highlight the importance of cautious and responsible deployment of GPT-4 Vision, ensuring that its use aligns with ethical standards and societal norms. 

 

Conclusion 

GPT-4 Vision represents a monumental leap in AI technology, merging text and image processing to offer unprecedented capabilities. Its potential in fields like web development, content creation, and data analysis is immense.

However, this technology comes with responsibilities. The potential risks, including privacy concerns, biases, and safety issues, underscore the importance of using GPT-4 Vision with a mindful approach.

As we harness this powerful tool, it’s crucial to continuously evaluate and address these challenges to ensure ethical and responsible usage of AI. 

In this blog, we are enhancing our Language Model (LLM) experience by adopting the Retrieval-Augmented Generation (RAG) approach!

We’ll explore the fundamental architecture of RAG conceptually and delve deeper by implementing it through the Lang Chain orchestration framework and leveraging an open-source model from Hugging Face for both question answering and text embedding. 

So, let’s get started! 

Common hallucinations in large language models  

The most common problem faced by state-of-the-art LLMs is that they produce inaccurate or hallucinated responses. This mostly occurs when prompted with information not present in their training set, despite being trained on extensive data.

 

 

This discrepancy between the general knowledge embedded in the LLM’s weights and newer information can be bridged using RAG. The solution provided by RAG eliminates the need for computationally intensive and expertise-dependent fine-tuning, offering a more flexible approach to adapting to evolving information.

 

Read more about: AI hallucinations and risks associated with large language models

 

 

 

What is RAG? 

Retrieval-Augmented Generation involves enhancing the output of Large Language Models (LLMs) by providing them with additional information from an external knowledge source.

 

Explore LLM context augmentation techniques like RAG and fine-tuning in detail with out podcast now!

 

This method aims to improve the accuracy and contextuality of LLM-generated responses while minimizing factual inaccuracies. RAG empowers language models to sidestep the need for retraining, facilitating access to the most up-to-date information to produce trustworthy outputs through retrieval-based generation. 

Architecture of RAG approach

Figure from Lang chain documentation

Prerequisites for code implementation 

1. HuggingFace account and LLAMA2 model access:

• Create a Hugging Face account (free sign-up available) to access open-source Llama 2 and embedding models. 
• Request access to LLAMA2 models using this form (access is typically granted within a few hours). 
• After gaining access to Llama 2 models, please proceed to the provided link, select the checkbox to indicate your agreement to the information, and then click ‘Submit’. 

2. Google Colab account:

• Create a Google account if you don’t already have one. 
• Use Google Colab for code execution. 

3. Google Colab environment setup: 

• In Google Colab, go to Runtime > Change runtime type > Hardware accelerator > GPU > GPU type > T4 for faster execution of code. 

4. Library and dependency installation: 

• Install necessary libraries and dependencies using the following command: 

 

5. Authentication with HuggingFace: 

• Integrate your Hugging Face token into Colab’s environment:

 

 

• When prompted, enter your Hugging Face token obtained from the “Access Token” tab in your Hugging Face settings. 

 

Step 1: Document Loading 

Loading a document refers to the process of retrieving and storing data as documents in memory from a specified source. This process is typically facilitated by document loaders, which provide a “load” method for accessing and loading documents into the memory. 

Lang chain has number of document loaders in this example we will be using “WebBaseLoader” class from the “langchain.document_loaders” module to load content from a specific web page.

 

 

 
The code extracts content from the web page “https://lilianweng.github.io/posts/2023-06-23-agent/“. BeautifulSoup (`bs4`) is employed for HTML parsing, focusing on elements with the classes “post-content”, “post-title”, and “post-header.” The loaded content is stored in the variable `docs`. 

 

 

Step 2: Document transformation – Splitting/chunking document 

After loading the data, it can be transformed to fit the application’s requirements or to extract relevant portions. This involves splitting lengthy documents into smaller chunks that are compatible with the model and produce accurate and clear results. Lang Chain offers various text splitters, in this implementation we chose the “RecursiveCharacterTextSplitter” for generic text processing.

 

 

The code breaks documents into chunks of 1000 characters with a 200-character overlap. This chunking is employed for embedding and vector storage, enabling more focused retrieval of relevant content during runtime. The recursive splitter ensures chunks maintain contextual integrity by using common separators, like new lines, until the desired chunk size is achieved. 

Step 3: Storage in vector database 

After extracting text chunks, we store and index them for future searches using the RAG application. A common approach involves embedding the content of each split and storing these embeddings in a vector store. 

When searching, we embed the search query and perform a similarity search to identify stored splits with embeddings most similar to the query embedding. Cosine similarity, which measures the angle between embeddings, is a simple similarity measure. 

Using the Chroma vector store and open source “HuggingFaceEmbeddings” in Lang chain, we can embed and store all document splits in a single command. 

Text embedding: 

Text embedding converts textual data into numerical vectors that capture the semantic meaning of the text. This enables efficient identification of similar text pieces. An embedding model, which is a variant of Language Models (LLMs) specifically designed for this purpose. 

 Lang Chain’s Embeddings class facilitates interaction with various text embedding models. While any model can be used, we opted for “HuggingFaceEmbeddings”. 

 

 

 

This code initializes an instance of the HuggingFaceEmbeddings class, configuring it with an open-source pre-trained model located at “sentence-transformers/all-MiniLM-l6-v2“. By doing this text embedding is created for converting textual data into numerical vectors. 

 

 

Vector Stores: 

Vector stores are specialized databases designed to efficiently store and search for high-dimensional vectors, such as text embeddings. They enable the retrieval of the most similar embedding vectors based on a given query vector. Lang Chain integrates with various vector stores, and we are using “Chroma” vector store for this task.

 

 

This code utilizes the Chroma class to create a vector store (vectorstore) from the previously split documents (splits) using the specified embeddings (embeddings). The Chroma vector store facilitates efficient storage and retrieval of document vectors for further processing. 

Step 4: Retrieval of text chunks 

After storing the data, preparing the LLM model, and constructing the pipeline, we need to retrieve the data. Retrievers serve as interfaces that return documents based on a query. 

Retrievers cannot store documents; they can only retrieve them. Vector stores form the foundation of retrievers. Lang Chain offers a variety of retriever algorithms, here is the one we implement. 

 

 

Step 5: Generation of answer with RAG approach 

Preparing the LLM Model: 

In the context of Retrieval Augmented Generation (RAG), an LLM model plays a crucial role in generating comprehensive and informative responses to user queries. By leveraging its ability to process and understand natural language, the LLM model can effectively combine retrieved documents with the given query to produce insightful and relevant outputs.

 

These lines import the necessary libraries for handling pre-trained models and tokenization. The specific model “meta-llama/Llama-2-7b-chat-hf” is chosen for its question-answering capabilities.

 

 

 

This code defines a transformer pipeline, which encapsulates the pre-trained HuggingFace model and its associated configuration. It specifies the task as “text-generation” and sets various parameters to optimize the pipeline’s performance. 

 

 

This line creates a Lang Chain pipeline (HuggingFace Pipeline) that wraps the transformer pipeline. The model_kwargs parameter adjusts the model’s “temperature” to control its creativity and randomness. 

Retrieval QA Chain: 

To combine question-answering with a retrieval step, we employ the RetrievalQA chain, which utilizes a language model and a vector database as a retriever. By default, we process all data in a single batch and set the chain type to “stuff” when interacting with the language model. 

 

 

 

 

 

This code initializes a RetrievalQA instance by specifying a chain type (“stuff”), a HuggingFacePipeline (llm), and a retriever (retriever-initialize previously in the code from vectorstore). The return_source_documents parameter is set to True to include source documents in the output, enhancing contextual information retrieval.
 

Finally, we call this QA chain with the specific question we want to ask.

 

 

The result will be: 

 

 

We can print source documents to see which document chunks the model used to generate the answer to this specific query.

 

 

 

 

In this output, only 2 out of 4 document contents are shown as an example, that were retrieved to answer the specific question. 

Conclusion 

In conclusion, by embracing the Retrieval-Augmented Generation (RAG) approach, we have elevated our Language Model (LLM) experience to new heights.

Through a deep dive into the conceptual foundations of RAG and practical implementation using the Lang Chain orchestration framework, coupled with the power of an open-source model from Hugging Face, we have enhanced question answering capabilities of LLMs.

This journey exemplifies the seamless integration of innovative technologies to optimize LLM capabilities, paving the way for a more efficient and powerful language processing experience. Cheers to the exciting possibilities that arise from combining innovative approaches with open-source resources!