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PaLM 2

Language models are a recent advanced technology that is blooming more and more as the days go by. These complex algorithms are the backbone upon which our modern technological advancements rest and are doing wonders for natural language communication.

From virtual assistants like Siri and Alexa to personalized recommendations on streaming platforms, chatbots, and language translation services, language models are surely the engines that power it all. 

The world we live in relies increasingly on natural language processing (NLP in short) for communication, information retrieval, and decision-making, making the evolution of language models not just a technological advancement but a necessity.

PaLM 2 vs. Llama 2
PaLM 2 vs. Llama 2

 

In this blog, we will embark on a journey through the fascinating world of language models and begin by understanding the significance of these models.  

 

 

 

 

But the real stars of this narrative will be PaLM 2 and Llama 2. These are more than just names; they are the cutting edge of NLP. PaLM 2 stands for “Progressive and Adaptive Language Model 2” and Llama 2 is short for “Language Learning and Mastery Algorithm 2”.

In the later sections, we will take a closer look at both these astonishing models by exploring their features and capabilities, and we will also do a comparison of these models by evaluating their performance, strengths, and weaknesses.

By the end of this exploration, we aim to shed light on which models might hold an edge or where they complement each other in the grand landscape of language models. 

 

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Before getting into the details of the PaLM 2 and Llama 2 models, we should have an idea of what language models are and what they have achieved for us.  

Language Models and their role in NLP 

Natural language processing (NLP) is a field of artificial intelligence which is solely dedicated to enabling machines and computers to understand, interpret, generate, and mimic human language.

And language models as we talk about, lie at the center of NLP, they are the heart of NLP and are designed to predict the likelihood of a word or a phrase given the context of a sentence or a series of words. There are two main things or concepts when we talk about language models, they are: 

  • Predictive Power: Language models excel in predicting what comes next in a sequence of words, making them incredibly useful in autocomplete features, language translation, and chatbots.
  • Statistical Foundation: Most language models are built on statistical principles, analyzing large corpora of text to learn the patterns, syntax, and semantics of human language.


Evolution of language models: From inception to the present day
 

These models have come a very long way since their birth, and their journey can be roughly divided into several generations, where some significant advancements were made in each generation. 

  • First Generation: Early language models used simple statistical techniques like n-grams to predict words based on the previous ones.
  • Second Generation: The advent of deep learning and neural networks revolutionized language models, giving rise to models like Word2Vec and GloVe, which had the ability to capture semantic relationships between words. 
  • Third Generation: The introduction of recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM) networks allowed models to better handle sequences of text, enabling applications like text generation and sentiment analysis. 
  • Fourth Generation: Transformer models, such as GPT (Generative Pre-trained Transformer), marked a significant and crucial leap forward in technology. These models introduced attention mechanisms, giving them the power to capture long-range dependencies in text and perform tasks ranging from translation to question-answering. 

 

Importance of recent advancements in language model technology

The recent advancements in language model technology have been nothing short of revolutionary, and they are transforming the way we used to interact with machines and access information from them. Here are some of the evolutions and advancements: 

  • Broader Applicability: The language models we have today can tackle a wider range of tasks, from summarizing text and generating code to composing poetry and simulating human conversation. 
  • Zero-shot Learning: Some models, like GPT-3 (by OpenAI), have demonstrated the ability to perform tasks with minimal or no task-specific training, showcasing their adaptability. 
  • Multimodal Integration: Language models are also starting to incorporate images, enabling them to understand and generate text based on visual content. 

This was all for a brief introduction into the world of language models and how they have evolved over the years, understanding these foundations of language models is essential as now we will be diving deeper into the latest innovations of PaLM 2 and Llama 2. 

 

Introducing PaLM 2

 

The term PaLM 2 as mentioned before is short for “Progressive and Adaptive Language Model 2”, and it is a groundbreaking language model which takes us to the next step in the evolution of NLP. Acquiring the knowledge of the successes from its predecessor models, PaLM model aims to push the boundaries of what’s possible in natural language generation, understanding and interpretation. 

Key Features and Capabilities of PaLM 2: 

PaLM 2 is not just another language model; it’s a groundbreaking innovation in the world of natural language processing and boasts a wide range of remarkable features and capabilities that sets it far apart from its predecessor models. Here, we’ll explore the distinctive features and attributes that make PaLM 2 stand out in the ever-competitive landscape of language models: 

Progressive Learning:

This model has the power to continually learn and adapt to changing language patterns, which in turn, ensures its relevance in a dynamic linguistic landscape. This ability of adaptability makes it well-suited for applications where language evolves rapidly, such as social media and online trends. 

Multimodal Integration:

The model can seamlessly integrate text and visual information, revealing many new possibilities in tasks that require a deep understanding of both textual and visual content. This feature is invaluable and priceless in fields like image captioning and content generation. 

Few-shot and Zero-shot Learning:

PaLM 2 demonstrates impressive few-shot and zero-shot learning abilities, which allows it to perform tasks with minimal examples or no explicit training data. This versatility makes it a valuable tool for a wide range of industries and applications. This feature reduces the time and resources needed for model adaptation. 

 

Scalability:

The model’s architecture is extremely efficient and is designed to scale efficiently, accommodating large datasets and high-performance computing environments. This scalability is essential for handling the massive volumes of text and data generated daily on the internet. 

Real-time applications:

PaLM 2’s adaptive nature makes it ideal for real-time applications, where staying aware of evolving language trends is crucial. Whether it’s providing up-to-the-minute news summaries, moderating online content, or offering personalized recommendations, PaLM 2 can excel greatly in real-time scenarios. 

Ethical considerations:

PaLM 2 also incorporates ethical guidelines and safeguards to address concerns about misinformation, bias, and inappropriate content generation. The developers have taken a proactive stance to ensure responsible AI practices are embedded in PaLM 2’s functionality.  

Real-world applications and use cases of PaLM 2: 

The features and capabilities of PaLM 2’s model extends to a myriad of real-world applications, revolutionizing and changing the way we interact with technology. You can see below some of the real-world applications for which this model has shown amazing wonders: 

  1. Content ceneration: Content creators can leverage PaLM 2 to automate content generation, from writing news articles and product descriptions to crafting creative marketing copy. 
  2. Customer support: PaLM 2 can power chatbots and virtual assistants, enhancing customer support by providing quick and accurate responses to the user inquiries.
  3. Language translation: Its multilingual proficiency makes it a valuable tool for translation services, enabling seamless communication across language barriers.
  4. Healthcare and research: In the medical field, PaLM 2 can assist in analyzing medical literature, generating reports, and even suggesting treatment options based on the latest research.
  5. Education: PaLM 2 can play a role in personalized education by creating tailored learning materials and providing explanations for complex topics. 

In conclusion, PaLM 2, is not merely a language model and is not like the predecessor models; it’s a visionary leap forward in the realm of natural language processing.

With its progressive learning, dynamic adaptability, multimodal integration, mastery of few-shot and zero-shot learning, scalability, real-time applicability, and ethical consciousness, PaLM 2 has redefined the way we used to interact with and harnessed the power of language models.

Its ability to evolve and adapt in real-time, coupled with its ethical safeguards, sets it apart as a versatile and responsible solution for a wide array of industries and applications.  

 

Meet Llama 2:  

 

Let’s talk about Llama 2 now, that is short for “Language Learning and Mastery Algorithm 2” and emerges as a pivotal player in the realm of language models. The model has been built upon the foundations laid by its predecessor model known as Llama. It is another one of the latest advanced models and introduces a host of enhancements and innovations poised to redefine the boundaries of natural language understanding and generation. 

Key features and capabilities of Llama 2: 

 

Beyond its impressive features, Llama 2 unveils a range of unique qualities that distinguish it as an exceptional contender in the world of language models. It distinguishes itself through its unique features and capabilities and here, we will discuss and highlight some of them briefly: 

  1. Semantic mastery: Llama 2 exhibits an exceptional grasp of semantics, allowing it to comprehend context and nuances in language with a depth that closely resembles human understanding and interpretation. This profound linguistic feature makes it a powerful tool for generating contextually relevant text. 
  2. Interdisciplinary proficiency: One of Llama 2’s standout attributes is its versatility across diverse domains, applications, and industries. Its adaptability renders it well-suited for a multitude of applications, spanning from medical research and legal documentation to creative content generation. 
  3. Multi-Language competence: The advanced model showcases an impressive multilingual proficiency, transcending language barriers to provide precise, accurate, context-aware translations and insights across a wide spectrum of languages. This feature greatly enables fostering global communication and collaboration.
  4. Conversational excellence: Llama 2 also excels in the realm of human-computer conversation. Its ability to understand conversational cues, context switches, and generate responses with a human touch makes it invaluable for applications like chatbots, virtual assistants, and customer support.
  5. Interdisciplinary collaboration: Another amazing aspect of Llama 2 is interdisciplinary collaboration as this model bridges the gap between technical and non-technical experts. This enables professionals from different fields to leverage the model’s capabilities effectively for their respective domains.
  6. Ethical focus: Like PaLM 2, Llama 2 also embeds ethical guidelines and safeguards into its functioning to ensure responsible and unbiased language processing, addressing the ethical concerns associated with AI-driven language models. 

 

Read more –> Boost your business with ChatGPT: 10 innovative ways to monetize using AI

Real-world applications and use cases of Llama 2: 

The adaptability and capabilities of Llama 2 extend across a plethora of real-world scenarios, ushering in transformative possibilities for our interaction with language and technology. Here are some domains in which Llama 2 excels with proficiency: 

  1. Advanced healthcare assistance: In the healthcare sector, Llama 2 lends valuable support to medical professionals by extracting insights from complex medical literature, generating detailed patient reports, and assisting in intricate diagnosis processes.
  2. Legal and compliance support: Legal practitioners also benefit from Llama 2’s capacity to analyze legal documents, generate precise contracts, and ensure compliance through its thorough understanding of legal language.
  3. Creative content generation: Content creators and marketers harness Llama 2’s semantic mastery to craft engaging content, compelling advertisements, and product descriptions that resonate with their target audience.
  4. Multilingual communication: In an increasingly interconnected and socially evolving world, Llama 2 facilitates seamless multilingual communication, offering accurate translations and promoting international cooperation and understanding. 

In summary, Llama 2, emerges as a transformative force in the realm of language models. With its profound grasp of semantics, interdisciplinary proficiency, multilingual competence, conversational excellence, and a host of unique attributes, Llama 2 sets new standards in natural language understanding and generation.

Its adaptability across diverse domains and unwavering commitment to ethical considerations make it a versatile and responsible solution for a myriad of real-world applications, from healthcare and law to creative content generation and fostering global communication. 

Comparing PaLM 2 and Llama 2

  • Performance metrics and benchmarks. 
  • Strengths and weaknesses. 
  • How both stand up against each other w.r.t accuracy, efficiency, and scalability. 
  • User experiences and feedback. 
Feature PaLM 2 Llama 2
Model size 540 billion parameters 70 billion parameters
Training data 560 billion words 560 billion words
Architecture Transformer-based Transformer-based
Training method Self-supervised learning Self-supervised learning

Conclusion: 

In conclusion, both PaLM 2 and Llama 2 stand as pioneering language models with the capacity to reshape our interaction with technology and address critical global challenges.

PaLM 2, possessing greater power and versatility, boasts an extensive array of capabilities and excels at adapting to novel scenarios and acquiring new skills. Nevertheless, it comes with the complexity and cost of training and deployment.

On the other hand, Llama 2, while smaller and simpler, still demonstrates impressive capabilities. It shines in generating imaginative and informative content, all while maintaining cost-effective training and deployment.

The choice between these models hinges on the specific application at hand. For those seeking a multifaceted, safe model for various tasks, PaLM 2 is a solid pick. If the goal is a creative and informative content generation, Llama 2 is the ideal choice. Both PaLM 2 and Llama 2 remain in active development, promising continuous enhancements in their capabilities. These models signify the future of natural language processing, holding the potential to catalyze transformative change on a global scale.

 

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September 11, 2023

In this article, we are getting an overview of LLM and some of the best Large Language Models that exist today.

In 2024, Artificial Intelligence (AI) is a hot topic, captivating millions of people worldwide. AI’s remarkable language capabilities, driven by advancements in Natural Language Processing (NLP) and Large Language Models (LLMs) like ChatGPT from OpenAI, have contributed to its popularity.

LLM, like ChatGPT, LaMDA, PaLM, etc., are advanced computer programs trained on vast textual data. They excel in tasks like text generation, speech-to-text, and sentiment analysis, making them valuable tools in NLP. The model’s parameters enhance its ability to predict word sequences, improving accuracy and handling complex relationships.

Introducing large language models in NLP

Natural Language Processing (NLP) has seen a surge in popularity due to computers’ capacity to handle vast amounts of natural text data. NLP has been applied in technologies like speech recognition and chatbots. Combining NLP with advanced Machine Learning techniques led to the emergence of powerful Large Language Models (LLMs).

Trained on massive datasets of text, reaching millions or billions of data points, these models demand significant computing power. To put it simply, if regular language models are like gardens, Large Language Models are like dense forests.

 

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How do large language models do their work?

LLMs, powered by the transformative architecture of Transformers, work wonders with textual data. These Neural Networks are adept at tasks like language translation, text generation, and answering questions. Transformers can efficiently scale and handle vast text corpora, even in the billions or trillions.

Unlike sequential RNNs, they can be trained in parallel, utilizing multiple resources simultaneously for faster learning. A standout feature of Transformers is their self-attention mechanism, enabling them to understand language meaningfully, grasping grammar, semantics, and context from extensive text data.

The invention of Transformers revolutionized AI and NLP, leading to the creation of numerous LLMs utilized in various applications like chat support, voice assistants, chatbots, and more. In this article, we’ll explore five of the most advanced LLMs in the world as of 2023.

Best large language models (LLMs) in 2024

Best Large Language Models (LLMs) in 2024 | Data Science Dojo
Best large language models 2024

1. GPT-4

GPT-4 is the latest and most advanced large language model from OpenAI. It has over 1 trillion parameters, making it one of the largest language models ever created. GPT-4 is capable of a wide range of tasks, including text generation, translation, summarization, and question answering. It is also able to learn from and adapt to new information, making it a powerful tool for research and development.

Key features of GPT-4

What sets GPT-4 apart is its human-level performance on a wide array of tasks, making it a game-changer for businesses seeking automation solutions. With its unique multimodal capabilities, GPT-4 can process both text and images, making it perfect for tasks like image captioning and visual question answering. Boasting over 1 trillion parameters, GPT-4 possesses an unparalleled learning capacity, surpassing all other language models.

Moreover, it addresses the accuracy challenge by being trained on a massive dataset of text and code, reducing inaccuracies and providing more factual information. Finally, GPT-4’s impressive fluency and creativity in generating text make it a versatile tool for tasks ranging from writing news articles and generating marketing copy to crafting captivating poems and stories.

Applications of GPT-4

  • Research: GPT-4 is a valuable tool for research in areas such as artificial intelligence, natural language processing, and machine learning.
  • Development: GPT-4 can be used to generate code in a variety of programming languages, which makes it a valuable tool for developers.
  • Business: GPT-4 can be used to automate tasks that are currently performed by humans, which can save businesses time and money.
  • Education: GPT-4 can be used to help students learn about different subjects.
  • Entertainment: GPT-4 can be used to generate creative text formats, such as poems, code, scripts, musical pieces, emails, letters, etc.

2. GPT-3.5

GPT-3.5 is a smaller version of GPT-4, with around 175 billion parameters. It is still a powerful language model, but it is not as large or as advanced as GPT-4. GPT-3.5 is still under development, but it has already been shown to be capable of a wide range of tasks, including text generation, translation, summarization, and question-answering.

Key features of GPT-3.5

GPT-3.5 is a fast and versatile language model, outpacing GPT-4 in speed and applicable to a wide range of tasks. It excels in creative endeavors, effortlessly generating poems, code, scripts, musical pieces, emails, letters, and more. Additionally, GPT-3.5 proves adept at addressing coding questions. However, it has encountered challenges with hallucinations and generating false information. Like many language models, GPT-3.5 may produce text that is factually inaccurate or misleading, an issue researchers are actively working to improve.

Applications of GPT-3.5

  • Creative tasks: GPT-3.5 can be used to generate creative text formats, such as poems, code, scripts, musical pieces, emails, letters, etc.
  • Coding questions: GPT-3.5 can be used to answer coding questions.
  • Education: GPT-3.5 can be used to help students learn about different subjects.
  • Business: GPT-3.5 can be used to automate tasks that are currently performed by humans, which can save businesses time and money.

3. PaLM 2

PaLM 2 (Bison-001) is a large language model from Google AI. It is focused on commonsense reasoning and advanced coding. PaLM 2 has been shown to outperform GPT-4 in reasoning evaluations, and it can also generate code in multiple languages.

Key features of PaLM 2

PaLM 2 is an exceptional language model equipped with commonsense reasoning capabilities, enabling it to draw inferences from extensive data and conduct valuable research in artificial intelligence, natural language processing, and machine learning. Moreover, it boasts advanced coding skills, proficiently generating code in various programming languages like Python, Java, and C++, making it an invaluable asset for developers seeking efficient and rapid code generation.

Another notable feature of PaLM 2 is its multilingual competence, as it can comprehend and generate text in more than 20 languages. Furthermore, PaLM 2 is quick and highly responsive, capable of swiftly and accurately addressing queries. This responsiveness renders it indispensable for businesses aiming to provide excellent customer support and promptly answer employee questions. PaLM 2’s combined attributes make it a powerful and versatile tool with a multitude of applications across various domains.

Applications of PaLM 2

  • Research: PaLM 2 is a valuable tool for research in areas such as artificial intelligence, natural language processing, and machine learning.
  • Development: PaLM 2 can be used to generate code in a variety of programming languages, which makes it a valuable tool for developers.
  • Business: PaLM 2 can be used to automate tasks that are currently performed by humans, which can save businesses time and money.
  • Customer support: PaLM 2 can be used to provide customer support or answer questions from employees.

4. Claude v1

Claude v1 is a large language model from Anthropic. It is backed by Google, and it is designed to be a powerful LLM for AI assistants. Claude v1 has a context window of 100k tokens, which makes it capable of understanding and responding to complex queries.

Key features of Claude v1

Furthermore, Claude v1 boasts a 100k token context window, surpassing other language models, allowing it to handle complex queries adeptly. It excels in benchmarks, ranking among the most powerful LLMs. Comparable to GPT-4 in performance, Claude v1 serves as a strong alternative for businesses seeking a potent LLM solution.

Applications of Claude v1

  • AI assistants: Claude v1 is designed to be a powerful LLM for AI assistants. It can be used to answer questions, generate text, and complete tasks.
  • Research: Claude v1 can be used for research in areas such as artificial intelligence, natural language processing, and machine learning.
  • Business: Claude v1 can be used by businesses to automate tasks, generate text, and improve customer service.

 

Read more –> Introducing Claude 2: Dominating conversational AI with revolutionary redefinition

5. Cohere

Cohere is a company that provides accurate and robust models for enterprise generative AI. Its Cohere Command model stands out for accuracy, making it a great option for businesses.

Key features of Cohere

Moreover, Cohere offers accurate and robust models, trained on extensive text and code datasets. The Cohere Command model, tailored for enterprise generative AI, is accurate, robust, and user-friendly. For businesses seeking reliable generative AI models, Cohere proves to be an excellent choice.

Applications of Cohere

  • Research: Cohere models can be used for research in areas such as artificial intelligence, natural language processing, and machine learning.
  • Business: Cohere models can be used by businesses to automate tasks, generate text, and improve customer service.

6. Falcon

Falcon is the first open-source large language model on this list, and it has outranked all the open-source models released so far, including LLaMA, StableLM, MPT, and more. It has been developed by the Technology Innovation Institute (TII), UAE.

Key features of Falcon

  • Apache 2.0 license: Falcon has been open-sourced with Apache 2.0 license, which means you can use the model for commercial purposes. There are no royalties or restrictions either.
  • 40B and 7B parameter models: The TII has released two Falcon models, which are trained on 40B and 7B parameters.
  • Fine-tuned for chatting: The Falcon-40B-Instruct model is fine-tuned for most use cases, including chat.
  • Works in multiple languages: The Falcon model has been primarily trained in English, German, Spanish, and French, but it can also work in Italian, Portuguese, Polish, Dutch, Romanian, Czech, and Swedish languages.

7. Gemini

Gemini, a model developed by Google, is notable for its multimodal capabilities. This means Gemini can interpret and respond to various types of content, including text, video, audio, and code.

The architecture and training strategies of Gemini emphasize extensive contextual understanding, a feature that sets it apart from many other models. These capabilities make Gemini versatile, suitable for applications requiring a nuanced understanding of different data formats

8. LLaMA

LLaMA is a series of the best large language models developed by Meta. The models are trained on a massive dataset of text and code, and they can perform a variety of tasks, including text generation, translation, summarization, and question-answering.

Key features of LLaMA

  • 13B, 26B, and 65B parameter models: Meta has released LLaMA models in various sizes, from 13B to 65B parameters.
  • Outperforms GPT-3: Meta claims that its LLaMA-13B model outperforms the GPT-3 model from OpenAI which has been trained on 175 billion parameters.
  • Released for research only: LLaMA has been released for research only and can’t be used commercially.

9. Guanaco-65B

Guanaco-65B is an open-source large language model that has been derived from LLaMA. It has been fine-tuned on the OASST1 dataset by Tim Dettmers and other researchers.

Key features of Guanaco-65B

  • Outperforms ChatGPT: Guanaco-65B outperforms even ChatGPT (GPT-3.5 model) with a much smaller parameter size.
  • Trained on a single GPU: The 65B model has trained on a single GPU having 48GB of VRAM in just 24 hours.
  • Available for offline use: Guanaco models can be used offline, which makes them a good option for businesses that need to comply with data privacy regulations.

10. Vicuna 33B

Vicuna is another open-source large language model that has been derived from LLaMA. It has been fine-tuned using supervised instruction and the training data has been collected from sharegpt.com, a portal where users share their incredible ChatGPT conversations.

Key features of Vicuna 33B

  • 33 billion parameters: Vicuna is a 33 billion parameter model, which makes it a powerful tool for a variety of tasks.
  • Performs well on MT-Bench and MMLU tests: Vicuna has performed well on the MT-Bench and MMLU tests, which are benchmarks for evaluating the performance of large language models.
  • Available for demo: You can try out Vicuna by interacting with the chatbot on the LMSYS website.

11. MPT-30B

MPT-30B is another open-source large language model that has been developed by Mosaic ML. It has been fine-tuned on a large corpus of data from different sources, including ShareGPT-Vicuna, Camel-AI, GPTeacher, Guanaco, Baize, and other sources.

Key features of MPT-30B

  • 8K token context length: MPT-30B has a context length of 8K tokens, which makes it a good choice for tasks that require long-range dependencies.
  • Outperforms GPT-3: MPT-30B outperforms the GPT-3 model by OpenAI on the MT-Bench test.
  • Available for local use: MPT-30B can be used locally, that makes it a good option for businesses that need to comply with data privacy regulations.

12. Cohere

Cohere, on the other hand, focuses on providing enterprise LLM solutions that can be custom-trained and fine-tuned for specific company use cases.

Cohere’s models can be trained and tailored to suit a wide range of applications, from blogging and content writing to more complex tasks requiring deep contextual understanding. The company offers a range of models, including Cohere Generate, Embed, and Rerank, each designed for different aspects of language processing. Cohere stands out for its adaptability and ease of integration into various business processes, offering solutions that solve real-world problems with advanced AI capabilities

What are open-source large language models?

Open-source large language models refer to sophisticated AI systems like GPT-3.5, which have been developed to comprehend and produce human-like text by leveraging patterns and knowledge acquired from extensive training data.

Constructed using deep learning methods, these models undergo training on massive datasets comprising diverse textual sources, such as books, articles, websites, and various written materials.

Top open-source large language models

 

Model

 

Parameters Description
GPT-3/4 175B/100T Developed by OpenAI. Can generate text, translate languages, and answer questions.
LaMDA 137B Developed by Google. Can converse with humans in a natural-sounding way.
LLaMA 7B-65B Developed by Meta AI. Can perform various NLP tasks, such as translation and question answering.
Bloom 176B Developed by BigScience. Can be used for a variety of NLP tasks.
PaLM 540B Developed by Google. Can perform complex NLP tasks, such as reasoning and code generation.
Dolly 12B Developed by Databricks. Can follow instructions and complete tasks.
Cerebras-GPT 111M-13B Family of large language models developed by Cerebras. Can be used for research and development.

Wrapping up

In conclusion, Large Language Models (LLMs) are transforming the landscape of natural language processing, redefining human-machine interactions. Advanced models like GPT-3, GPT-4, Gopher, PALM, LAMDA, and others hold great promise for the future of NLP. Their continuous advancement will enhance machine understanding of human language, leading to significant impacts across various industries and research domains.

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July 26, 2023

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