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Ayesha Saleem

InstructGPT is an advanced iteration of the GPT (Generative Pretrained Transformer) language models developed by OpenAI. Here’s a detailed look into InstructGPT:

What is InstrcutGPT?

The main objective of InstructGPT is to better align AI-powered language models with human intentions by training them using Reinforcement Learning from Human Feedback (RLHF). This method improves the model’s ability to understand and follow instructions more accurately.

 

instructgpt

 

Target users

InstructGPT is built for a broad range of users, from developers creating AI applications to businesses leveraging AI for enhanced customer service and for educational purposes where clear, concise, and contextually correct language is crucial.

Key features

  • Alignment with Human Intent: The model is fine-tuned to understand and execute instructions as intended by the user.
  • Enhanced Accuracy and Relevance: Through self-evaluation and human feedback, InstructGPT provides responses that are more accurate and contextually relevant.
  • Instruction-based Task Performance: It is designed to perform structured tasks based on specific instructions.

Examples of use

  • Creating more effective chatbots that can understand and respond to user queries accurately.
  • Generating educational content that can help explain complex topics in a simple manner.
  • Assisting in programming by providing code explanations or generating code snippets based on a given prompt.
  • Enhancing customer service by providing precise answers to customer inquiries, reducing the need for human intervention.

InstructGPT represents a significant move towards creating AI that can interact with humans more naturally and effectively, leading to a wide array of practical applications across different industries

 

Read in detail about GPT 4 use cases

 

InstructGPT prompting examples

Prompt:
Tell a 6-year-old why the sky is blue in a few sentences.

Completion:
GPT-3:
Discuss the scientific reason for the sky’s color.
Describe how light travels from the sun to the earth.
Detail the process of atmospheric scattering of sunlight.

InstructGPT response:
The sky looks blue to us during the day because the sunlight gets scattered by stuff in the air, and blue light gets scattered the most, so that’s what we see!

 

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InstructGPT architecture

Let’s break down the architecture of InstructGPT in a way that’s easy to digest. Imagine that you’re building a really complex LEGO model. Now, instead of LEGO bricks, InstructGPT uses something called a transformer architecture, which is just a fancy term for a series of steps that help the computer understand and generate human-like text.

At the heart of this architecture are things called attention mechanisms. Think of these as little helpers inside the computer’s brain that pay close attention to each word in a sentence and decide which other words it should pay attention to. This is important because, in language, the meaning of a word often depends on the other words around it.

Now, InstructGPT takes this transformer setup and tunes it with something called Reinforcement Learning from Human Feedback (RLHF). This is like giving the computer model a coach who gives it tips on how to get better at its job. For InstructGPT, the job is to follow instructions really well.

So, the “coach” (which is actually people giving feedback) helps InstructGPT understand which answers are good and which aren’t, kind of like how a teacher helps a student understand right from wrong answers. This training helps InstructGPT give responses that are more useful and on point.

And that’s the gist of it. InstructGPT is like a smart LEGO model built with special bricks (transformers and attention mechanisms) and coached by humans to be really good at following instructions and helping us out.

 

Differences between InstructorGPT, GPT 3.5 and GPT 4

Comparing GPT-3.5, GPT-4, and InstructGPT involves looking at their capabilities and optimal use cases.

Feature InstructGPT GPT-3.5 GPT-4
Purpose Designed for natural language processing in specific domains General-purpose language model, optimized for chat Large multimodal model, more creative and collaborative
Input Text inputs Text inputs Text and image inputs
Output Text outputs Text outputs Text outputs
Training Data Combination of text and structured data Massive corpus of text data Massive corpus of text, structured data, and image data
Optimization Fine-tuned for following instructions and chatting Fine-tuned for chat using the Chat Completions API Improved model alignment, truthfulness, less offensive output
Capabilities Natural language processing tasks Understand and generate natural language or code Solve difficult problems with greater accuracy
Fine-Tuning Yes, on specific instructions and chatting Yes, available for developers Fine-tuning capabilities improved for developers
Cost Initially more expensive than base model, now with reduced prices for improved scalability

GPT-3.5

  • Capabilities: GPT-3.5 is an intermediate version between GPT-3 and GPT-4. It’s a large language model known for generating human-like text based on the input it receives. It can write essays, create content, and even code to some extent.
  • Use Cases: It’s best used in situations that require high-quality language generation or understanding but may not require the latest advancements in AI language models. It’s still powerful for a wide range of NLP tasks.

GPT-4

  • Capabilities: GPT-4 is a multimodal model that accepts both text and image inputs and provides text outputs. It’s capable of more nuanced understanding and generation of content and is known for its ability to follow instructions better while producing less biased and harmful content.
  • Use Cases: It shines in situations that demand advanced understanding and creativity, like complex content creation, detailed technical writing, and when image inputs are part of the task. It’s also preferred for applications where minimizing biases and improving safety is a priority.

 

Learn more about GPT 3.5 vs GPT 4 in this blog

 

InstructGPT

  • Capabilities: InstructGPT is fine-tuned with human feedback to follow instructions accurately. It is an iteration of GPT-3 designed to produce responses that are more aligned with what users intend when they provide those instructions.
  • Use Cases: Ideal for scenarios where you need the AI to understand and execute specific instructions. It’s useful in customer service for answering queries or in any application where direct and clear instructions are given and need to be followed precisely.

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When to use each

  • GPT-3.5: Choose this for general language tasks that do not require the cutting-edge abilities of GPT-4 or the precise instruction-following of InstructGPT.
  • GPT-4: Opt for this for more complex, creative tasks, especially those that involve interpreting images or require outputs that adhere closely to human values and instructions.
  • InstructGPT: Select this when your application involves direct commands or questions and you expect the AI to follow those to the letter, with less creativity but more accuracy in instruction execution.

Each model serves different purposes, and the choice depends on the specific requirements of the task at hand—whether you need creative generation, instruction-based responses, or a balance of both.

February 15
Fiza Author image
Fiza Fatima

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 from 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 models

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 and closed-source language models 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 and Closed Source LLMS

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

What are close-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.

5. Examples of close-source large language Mmodels

  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:

Open-Source LLMs Vs Close-Source LLMs for enterprises

 

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 offer 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 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.

February 15
Muneeb Alam
Muneeb Alam

Feature Engineering is a process of using domain knowledge to extract and transform features from raw data. These features can be used to improve the performance of Machine Learning Algorithms.

 

Feature Engineering encompasses a diverse array of techniques, including Feature Transformation, Feature Construction, Feature Selection, Feature Scaling, and Feature Extraction, each playing a crucial role in refining and optimizing the representation of data for machine learning tasks. 

 

In this blog, we will discuss one of the feature transformation techniques called feature scaling with examples and see how it will be the game changer for our machine learning model accuracy. 

 

Large language model bootcamp

 

In the world of data science and machine learning, feature transformation plays a crucial role in achieving accurate and reliable results. By manipulating the input features of a dataset, we can enhance their quality, extract meaningful information, and improve the performance of predictive models. Python, with its extensive libraries and tools, offers a streamlined and efficient process for simplifying feature scaling. 

 

What is feature scaling?

 

Feature scaling is a crucial step in the feature transformation process that ensures all features are on a similar scale. It is the process that normalizes the range of input columns and makes it useful for further visualization and machine learning model training. The figure below shows a quick representation of feature scaling techniques that we will discuss in this blog.

 

feature scaling techniques
A visual representation of feature scaling techniques – Source: someka.net

 

Why feature scaling is important?

 

Feature scaling is important because of several factors:

  • It improves the machine learning model’s accuracy
  • It enhances the interpretability of data by transforming features on a common scale, without scaling, it is difficult to make comparisons of two features because of scale difference
  • It speeds up the convergence in optimization algorithms like gradient descent algorithms
  • It reduces the computational resources required for training the model
  • For better accuracy, it is essential for the algorithms that rely on distance measures, such as K-nearest neighbors (KNN) and Support Vector Machines (SVM), to be sensitive to feature scales

 

Now let’s dive into some important methods of feature scaling and see how they impact data understanding and machine learning model performance.

 

Normalization

 

A feature scaling technique is often applied as part of data preparation for machine learning. The goal of normalization is to change the value of numeric columns in the dataset to use a common scale, without distorting differences in the range of values or losing any information.

 

Min-Max Scaler

 

The most commonly used normalization technique is min-max scaling, which transforms the features to a specific range, typically between 0 and 1. Scikit-learn has a built-in class available named MinMaxScaler that we can use directly for normalization. It involves subtracting the minimum value and dividing by the range of the feature using this formula.

 

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Where,

Xi is the value we want to normalize.

Xmax is the maximum value of the feature.

Xmin is the minimum value of the feature.

 

In this transformation, the mean and standard deviation of the feature may behave differently. Our main focus in this normalization is on the minimum and maximum values. Outliers may disrupt our data pattern, so taking care of them is necessary.

 

Let’s take an example of a wine dataset that contains various ingredients of wine as features. We take two input features: the quantity of alcohol and malic acid and create a scatter plot as shown below.

 

feature scaling - normalization
Scatter plot from the wine dataset

 

When we create a scatter plot between alcohol and malic acid quantities, we can see that min-max scaling simply compresses our dataset into the range of zero to one, while keeping the distribution unchanged.

 

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Standardization

 

Standardization is a feature scaling technique in which values of features are centered around the mean with unit variance. It is also called Z-Score Normalization. It subtracts the mean value of the feature and divides by the standard deviation (σ) of the feature using the formula:

 

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Here we leverage a dataset on social network ads to gain a practical understanding of the concept. This dataset includes four input features: User ID, Gender, Age, and Salary. Based on this information, it determines whether the user made a purchase or not (where zero indicates not purchased, and one indicates purchased).

 

The first five rows of the dataset appear as follows:

 

dataset for standardization
Dataset for the standardization example

 

In this example, we extract only two input features (Age and Salary) and use them to determine whether the output indicates a purchase or not as shown below.

 

data for standardization

Standard Scaler

 

We use Standard-Scaler from the Scikit-learn preprocessing module to standardize the input features for this feature scaling technique. The following code demonstrates this as shown.

 

 

We can see how our features look before and after standardization below.

 

data before and after standardization

 

Although it appears that the distribution changes after scaling, let’s visualize both distributions through a scatter plot.

 

visual representation of impact of scaling on data
Visual representation of the impact of scaling on data

 

So, when we visualize these distributions through plots, we observe that they remain the same as before. This indicates that scaling doesn’t alter the distribution; it simply centers it around the origin.

Now let’s see what happens when we create a density plot between Age and Estimated Salary with and without scaled features as shown below.

 

density plots for standardization
Graphical representation of data standardization

 

In the first plot, we can observe that we are unable to visualize the plot effectively and are not able to draw any conclusions or insights between age and estimated salary due to scale differences. However, in the second plot, we can visualize it and discern how age and estimated salary relate to each other.

 

This illustrates how scaling assists us by placing the features on similar scales. Note that this technique does not have any impact on outliers. So, if an outlier is present in the dataset, it remains as it is even after standardization. Therefore, we need to address outliers separately.

 

Model’s performance comparison

 

Now we use the logistic regression technique to predict whether a person will make a purchase after seeing an advertisement and observe how the model behaves with scaled features compared to without scaled features.

 

 

Here, we can observe a drastic improvement in our model accuracy when we apply the same algorithm to standardized features. Initially, our model accuracy is around 65.8%, and after standardization, it improves to 86.7% 

 

When does it matter?

 

Note that standardization does not always improve your model accuracy; its effectiveness depends on your dataset and the algorithms you are using. However, it can be very effective when you are working with multivariate analysis and similar methods, such as Principal Component Analysis (PCA), Support Vector Machine (SVM), K-means, Gradient Descent, Artificial Neural Networks (ANN), and K-nearest neighbors (KNN).

 

However, when you are working with algorithms like decision trees, random forest, Gradient Boosting (G-Boost), and (X-Boost), standardization may not have any impact on improving your model accuracy as these algorithms work on different principles and are not affected by differences in feature scales

 

To sum it up

 

We have covered standardization and normalization as two methods of feature scaling, including important techniques like Standard Scaler and Min-Max Scaler. These methods play a crucial role in preparing data for machine learning models, ensuring features are on a consistent scale. By standardizing or normalizing data, we enhance model performance and interpretability, paving the way for more accurate predictions and insights.

February 13
Huda Mahmood - Author
Huda Mahmood

The development of generative AI relies on important machine-learning techniques in today’s technological advancement. It makes machine learning (ML) a critical component of data science where algorithms are statistically trained on data.

 

An ML model learns iteratively to make accurate predictions and take actions. It enables computer programs to perform tasks without depending on programming. Today’s recommendation engines are one of the most innovative products based on machine learning.

 

Exploring important machine-learning techniques

 

The realm of ML is defined by several learning methods, each aiming to improve the overall performance of a model. Technological advancement has resulted in highly sophisticated algorithms that require enhanced strategies for training models.

 

Let’s look at some of the critical and cutting-edge machine-learning techniques of today.

 

Transfer learning

 

This technique is based on training a neural network on a base model and using the learning to apply the same model to a new task of interest. Here, the base model represents a task similar to that of interest, enabling the model to learn the major data patterns.

 

one of the machine-learning techniques
A visual understanding of transfer learning – Source: Medium

 

  • Why use transfer learning? 

    It leverages knowledge gained from the first (source) task to improve the performance of the second (target) task. As a result, you can avoid training a model from scratch for related tasks. It is also a useful machine-learning technique when data for the task of interest is limited.

 

  • Pros 

    Transfer learning enhances the efficiency of computational resources as the model trains on target tasks with pre-learned patterns. Moreover, it offers improved model performance and allows the reusability of features in similar tasks.

 

  • Cons 

    This machine-learning technique is highly dependent on the similarity of two tasks. Hence, it cannot be used for extremely dissimilar and if applied to such tasks, it risks overfitting the source task during the model training phase.

 

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Fine-tuning

 

Fine-tuning is a machine-learning technique that aims to support the process of transfer learning. It updates the weights of a model trained on a source task to enhance its adaptability to the new target task. While it looks similar to transfer learning, it does not involve replacing all the layers of a pre-trained network.

 

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Fine-tuning: Improving model performance in transfer learning – Source: Analytics Yogi

 

  • Why use fine-tuning? 

    It is useful to enhance the adaptability of a pre-trained model on a new task. It enables the ML model to refine its parameters and learn task-specific patterns needed for improved performance on the target task.

 

  • Pros 

    This machine-learning technique is computationally efficient and offers improved adaptability to an ML model when dealing with transfer learning. The utilization of pre-learned features becomes beneficial when the target task has a limited amount of data.

 

  • Cons 

    Fine-tuning is sensitive to the choice of hyperparameters and you cannot find the optimal settings right away. It requires experimenting with the model training process to ensure optimal results. Moreover, it also has the risk of overfitting and limited adaptation in case of high dissimilarity in source and target tasks.

 

Multitask learning

 

As the name indicates, the multitask machine-learning technique unlocks the power of simultaneity.  Here, a model is trained to perform multiple tasks at the same time, sharing the knowledge across these tasks.

 

multitask learning - a type of machine-learning technique
Understanding multitask learning – Source: ResearchGate

 

  • Why use multitask learning? 

    It is useful in sharing common representations across multiple tasks, offering improved generalization. You can use it in cases where several related ML tasks can benefit from shared representations.

 

  • Pros 

    The enhanced generalization capability of models ensures the efficient use of data. Leveraging information results in improved model performance and regularization of training. Hence, it results in the creation of more robust training models.

 

  • Cons 

    The increased complexity of this machine-learning technique requires advanced architecture and informed weightage of different tasks. It also depends on the availability of large and diverse datasets for effective results. Moreover, the dissimilarity of tasks can result in unwanted interference in the model performance of other tasks.

 

Federated learning

 

It is one of the most advanced machine-learning techniques that focuses on decentralized model training. As a result, the data remains on the user-end devices, and the model is trained locally. It is a revolutionized ML methodology that enhances collaboration among decentralized devices.

 

federated learning
Federated learning: A revolutionary ML technique – Source: Sony AI

 

  • Why use federated learning?Federated learning is focused on locally trained models that do not require the sharing of raw data of end-user devices. It enables the sharing of key parameters through ML models while not requiring an exchange of sensitive data.

 

  • ProsThis machine-learning technique addresses the privacy concerns in ML training. The decentralized approach enables increased collaborative learning with reduced reliance on central servers for ML processes. Moreover, this method is energy-efficient as models are trained locally.

 

  • ConsIt cannot be implemented in resource-constrained environments due to large communication overhead. Moreover, it requires compatibility between local data and the global model at the central server, limiting its ability to handle heterogeneous datasets.

 

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Factors determining the best machine-learning technique

 

While there are numerous machine-learning techniques available for model training today, it is crucial to make the right choice for your business. Below is a list of important factors that you must consider when selecting an ML method for your processes.

 

 

Context matters!

 

Context refers to the type of problem or task at hand. The requirements and constraints of the model-training process is pivotal in choosing an ML technique. For instance, transfer learning and fine-tuning promote knowledge sharing, multitask learning promotes simultaneity, and federated learning supports decentralization.

 

Data availability and complexity

 

ML processes require large datasets to develop high-performing models. Hence, the amount and complexity of data determine the choice of method. While transfer learning and multitask learning need large amounts of data, fine-tuning is suitable for a limited dataset. Moreover, data complexity determines knowledge sharing and feature interactions.

 

Computational resources

 

Large neural networks and complex machine-learning techniques require large computational power. The availability of hardware resources and time required for training are important measures of consideration when making your choice of the right ML method.

 

Data privacy considerations

 

With rapidly advancing technological processes, ML and AI have emerged as major tools that heavily rely on available datasets. It makes data a highly important part of the process, leading to an increase in privacy concerns and protection of critical information. Hence, your choice of machine-learning technique must fulfill your data privacy demands.

 

Make an informed choice!

 

Important machine-learning techniques
An outlook of important machine-learning techniques

 

In conclusion, it is important to understand the specifications of the four important machine-learning techniques before making a choice. Each method has its requirements and offers unique benefits. It is crucial to understand the dimensions of each technique in the light of key considerations discussed above. Hence, make an informed choice for your ML training processes.

February 9
Fiza Author image
Fiza Fatima

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.

Mixtral AI Framework
Mixtral AI Framework – Source: Mistral AI

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.

  1. 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.

Mixtral 8x7B Vs Llama 2 70b, ChatGPT 3.5 - Source: Mistral AI
Mixtral 8x7B Vs Llama 2 70b, ChatGPT 3.5 – Source: Mistral AI

 

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.

Hallucinations - Mixtral 8x7B Vs Llama 2 70b - Source: Mistral AI
Hallucinations – Mixtral 8x7B Vs Llama 2 70b – Source: Mistral AI

 Multilingualism

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

Hallucinations - Mixtral 8x7B Vs Llama 2 70b - Source: Mistral AI
Mixtral 8x7B Vs Llama 2 70b, ChatGPT 3.5 – Source: Mistral AI

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.

February 9
Data Science Dojo
Ayesha Aamir

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.

 

embedding techniques
Popular word embedding techniques

 

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.

 

Large language model bootcamp

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.

 

Learn to build LLM applications

 

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.

February 8
Izma Aziz
Izma Aziz

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