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| LLM Featured |
Introduction
As of 2023, Artificial Intelligence is taking the world by storm. It has become a hot topic of discussion, capturing the attention of millions of people not just technical experts and researchers but also individuals from diverse backgrounds. One of the reasons for this hype in AI to the capabilities it possesses in the fields humans are dealing with for hundreds of years in various forms, the Language. Language is an integral part of human life, It helps us to communicate, make sense of stuff around us, and even help us think. But, today AI becomes more capable of handling language at the human level or maybe above the human level. This is possible because of the advancement in Natual Language Processing(NLP) and Large Language Models(LLMs), the one behind ChatGPT, the greatest creation from San Fransisco-based startup, OpenAI. But, OpenAI became one of the successful companies to roll out their LLM technology to the public. There are a ton of such types of Large Language Models exists built by large and small companies. In this article, we are getting an overview of Large Language Models and some of the advanced LLMs that exist in the world, To be precise, we'll discuss 5 of them. Note that this list of LLMs is made by research from various sources and not on a rank basis.
The Essence of Large Language Models
In recent years, Natural Language Processing (NLP) has experienced a surge in popularity because of the ability of computers to store and process large amounts of natural text data. The applications of NLP can be seen in various technologies we are using over decades like speech recognition, chatbots, etc. Since Machine Learning emerged, Scientists began to club NLP with the most advanced Machine Learning techniques to process text much more efficiently. But, recently NLP is firing in great popularity primarily due to the emergence of powerful Large Language Models (LLMs).
So what are Large Language Models and why they are so much powerful? Language Models are basically a special type of machine learning model that can learn, understand, and process human language efficiently. By learning from datasets containing texts, Language Models can predict the next word or sentence with great accuracy. But, When they become Large, they are even more interesting and special. LLMs are trained on very large datasets of text(millions or billions of text data) and require massive computing power. To compare, if language models are like gardens, then Large Language Models are like dense forests.
How do LLMs do their work?
As we said, LLMs are Machine Learning models which can do a lot of things with text, like translating one language to another, generating language, answering questions, etc. But how do they do that? The possibility of building LLMs came from a special type of Neural Network architecture proposed by Google Researchers, called the Transformers.
Transformers are a special type of Neural Network which is specifically made for doing magic with textual data. They are very capable of scaling effectively and can be trained on a very large corpus of text, even billion or trillions of text!. Also, transformers can be trained more fastly than other types of Neural Networks like RNN(Recurrent Neural Networks) on large text data. Even more interestingly, Transformers can be trained in parallel, meaning that multiple computational resources, such as CPUs or GPUs, can be utilized simultaneously to accelerate the learning process, unlike RNN which can only process data sequentially.
Another interesting specialty of Transformer models is the self-attention technique, This mechanism allows transformers to learn the underlying meaning of the language rather than just producing randomly related text one by one. And because of this capability, today's Language Models are not only just spitting text one by one, but they are learning the actual meaning of a language(Like humans do) including the grammar, semantics, and context of text by giving large amounts of text data to learn.
The invention of the Transformer model by Google made a great achievement in AI and the field of Natural Language Processing(NLP). Using this Transformer model, many large, small, and even startups are building LLMs and using it for different purposes, like technical chat support, voice assistants, content generation, chatbots, and many many more. We cannot discuss every LLMs that exists today, because there are a ton of them. So, right now, let's discuss 5 of the most advanced LLMs that exist in the world as of 2023,
Top 5 State-of-the-art LLMs today
1. GPT-4 (OpenAI)
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| GPT-4 |
GPT-4 short for Generative Pre-trained Transformer-4 is OpenAI's most advanced and highly sophisticated Large Language Model. It is the fourth series of language models released on March 14, 2023, after the successful launch of ChatGPT powered by GPT-3.5. It is equipped with state-of-the-art reasoning and creative capabilities which are apart from one's imagination. GPT-4 is a massive Neural Network containing an impressive 100 trillion parameters and trained on a very large corpus of text data including code from various programming languages. Moreover, GPT-4 is not only proficient in processing text but also exhibits the capability to handle visual data, including images. With its ability to understand and generate content from both text and visual inputs, GPT-4 can be considered a powerful multimodal AI, bridging the domains of language and vision.
Another interesting capability of GPT-4 is the amount of data it can process in a single request. The predecessor language models from OpenAI can process up to 3,000 tokens in a single request, but GPT-4 can process up to 25,000 tokens per request. This is very large that you can actually ask GPT-4 to summarize an entire 10-page PDF in one go.
Even more interesting, Scientists and researchers at OpenAI say that GPT-4 has a glimpse of Artificial General Intelligence(AGI), which many scientists thought might be impossible in the next 40 or 50 years. However, GPT-4 is not a perfect system, as per the blog from OpenAI, GPT-4 is likely for hallucinations and false responses.
2. GPT-3 (OpenAI)
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| GPT-3 |
GPT-3(Generative Pre-trained Transformer 3), another impressive Transformer based LLM introduced by OpenAI on June 11, 2020, continues to stand out as one of the most advanced LLMs available in the market as of 2023. It uses advanced deep learning techniques, such as transformers and attention mechanisms, to process and generate text that is indistinguishable from human-written text.
At its core, GPT-3 is large, with around 175 billion parameters, uses advanced Natural Language Processing(NLP), and is undergone training on a dataset containing terabytes of text data from various sources like Wikipedia, WebText2, books, articles, and code. This complexity makes GPT-3 more capable surpassing its predecessor, GPT-2, by a factor of 10. As a result, GPT-3 has demonstrated exceptional abilities in language processing, including text generation, language translation, and question answering. Additionally, GPT-3 has undergone extensive training on a vast portion of Github, which has equipped it with expertise in coding across a wide range of programming languages and concepts.
After the success of GPT-3, the company again launched the enhanced version of GPT-3 known as GPT-3.5 which is powering ChatGPT.
3. Gopher (DeepMind)
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| Gopher |
Developed by Google DeepMind, A subsidiary company of Alphabet Inc, and one of the top-notch AI research labs in the world, Gopher is an AI language model which is specifically trained on tasks like reading comprehension, fact-checking, understanding toxic language, and logical and common sense tasks.
Researchers and developers at DeepMind developed a series of language models ranging from 44 million parameters to 280 billion parameters trained on a large corpus of text from various sources. Among those language models, the 280 billion parameter model showed greater capabilities in language understanding and generation, and they named it Gopher. In their research, they found that Gopher exceeds existing language models in a variety of tasks and reached human-level expertise, including Massive Multitask Language Understanding(MMLU) which is a term given to the new benchmark designed to measure the ability of large language models to understand and respond to a wide range of language tasks. This research shows that Gopher performs well in fields like math, science, technology, Humanities, and Medicine than other language models including GPT-3.
Gopher is designed to excel in dialogue-based interactions, enabling it to provide explanations on even complex subjects through chat-like responses. You can see the example of Gopher explaining cell biology in really simple terms if you visit their company blog.
4. PaLM (Google)
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| Pathways Language Model |
PaLM, short for Pathways Language Model is one of Google's advanced language models which is developed to generalize across multiple domains within a single model. It uses Pathways architecture for a better understanding of language and eliminates some of the limitations of existing language models like domain-specificity, unimodality, etc. Pathways is a Neural Network architecture that is relatively new and ongoing research in Google. Pathways allow AI systems to excel across multiple domains rather than focusing on a single set of tasks. It also allows AI models to be multimodal, meaning they can process and understand information from various modalities such as text, images, and audio simultaneously.
PaLM is a 540-billion parameter Transformer based language model which is better at understanding language and also good at state-of-the-art performance in various domains including language understanding, question answering, arithmetic, code, language translation, logical reasoning, dialogue, etc. Even more interesting, Google researchers and developers integrated their PaLM model into a real-world robot by adding sensory information and robotic gestures and control. This robot can do a variety of tasks with its PaLM brain including natural language understanding to perform real-world tasks, For instance, it can engage in meaningful conversations with humans, understand and respond to spoken commands, navigate its environment autonomously, manipulate objects using robotic arms, and perform a wide range of real-world tasks.
PaLM is one of the most actively pursued research areas at Google, and the company is developing new and highly capable versions of PaLM, as a matter of fact, they recently launched PaLM-2 which has impressive reasoning, coding, and Multilingual capabilities.
5. LaMDA(Google)
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| Language Model for Dialogue Applications |
LaMDA or Language Model for Dialogue Applications is another Language Model developed by Google in their early research in 2020. Unlike other language models, LaMDA is trained mostly on dialogue-based text which is better for conversations. Since it is trained in dialogue, LaMDA showed exceptional skills in having meaningful conversations at a human level. This ability of LaMDA is really great and one of the former employees at Google actually thought that LaMDA is sentient.
LaMDA is built on advanced NLP techniques with Transformer based Neural Network model. As per Google researchers, combining the transformer-based model with dialogue could potentially make Large Langauge Models better at human-level conversations and also eventually could learn to talk about virtually anything. Also, after training in a massive amount of dialogue text, LaMDA can also be fine-tuned using reinforcement learning to make it more difficult for humans to distinguish AI in conversation-based tasks.
In February 2023, Google integrated their recent version of LaMDA into their chatbot, Bard which is now available worldwide, but Google says, they replaced the technology behind Bard from LaMDA to PaLM-2.
Additional Notable Mentions
LLaMA (Meta AI)
LLaMA (Large Language Model Meta AI) is a family of open-source LLMs developed by Meta, formerly known as Facebook. LLaMA 1, introduced in February 2023, is considered one of the best open-source language models available that can be used for various NLP tasks without any cost unless you may require a GPU to run at your home. The first version of LLaMA comes with different variants including the 7, 13, 33, and 65 billion parameters models. Among them, Meta researchers found that the model with 13 billion parameters performs better in most of the NLP tasks comparable to GPT-3 (175 billion). The 65 billion models perform even better which can be a competition for PaLM models from Google.
On July 2023, Meta AI in partnership with Microsoft released LLaMA 2 in three variants 7, 13, and 70 billion parameter models. This was a great gift to the open-source AI community where everyone in the whole world can use a model which has abilities comparable to most of the closed-source LLMs. These variants are trained on more data than the previous LLaMA 1 models keeping the architecture of the models the same. However, LLaMA 2 models are ranked on top of hugging face Open_LLM_Leaderboard, and you can still find it by visiting hugging face.
Claude (Anthropic)
Claude is a chat-like LLM introduced by Anthropic, a new AI research startup company created by former members of OpenAI. Claude is developed to engage in conversation along with security concerns. It is also able to do a lot of NLP tasks like other LLMs like summarization, generating new ideas, translation, reasoning, coding, etc.
Claude is accessible via API and through the Anthropic chat interface. They have two versions of Claude called the Claude and Claude Instant, Claude is the most capable and powerful model while Claude Instant can be used for lightweight tasks. The interesting thing is that Claude can be seen in many apps that we might use every day, for example, Notion AI is based on Claude which brings the power of AI and productivity together.
Additionally, you can easily find Claude on Quora, where if you search for some questions, you'll see a bot answering alongside human conversations. That is an example of Claude in action.
Falcon (TII)
Falcon is another new open-source LLM released under Apache 2.0 license, which means the model is completely open-source, and you can use it for whatever you want. It is trained by the Technology Innovative Institute (TII) in UAE. Falcon is considered a state-of-the-art open-source LLM that showed remarkable performance in many tasks including human-level conversation.
Falcon is trained with a feature called multi-query attention which shares the key value vectors across multiple heads. This means you can train the attention in parallel without too much computation and complexity. Falcon comes in two different sizes, the 7 billion parameters which can be used for simple tasks like chatting, simple reasoning, question answering, etc. The 40 billion parameter model can be used for more language-intensive tasks like generation, translation, reasoning, etc. Both of these models are trained on a large corpus of text data approximately, a trillion tokens.
When Falcon was released for the first time, it ranks on the top of the hugging face LLM leaderboard in case of performance soon replaced by LLaMA. However, Falcon is highly flexible and can be fine-tuned for your own purpose. There are many state-of-the-art chatbots you can find online that use fine-tuned Falcon models. Anyway, you can test Falcon either by going to the hugging face spaces or just downloading the model into your local system if your system has 90GB of GPU for the larger model Falcon 40b and 15GB for the smaller model Falcon 7b.
Conclusion
LLMs are revolutionizing natural language processing and changing the way humans and machines interact with each other. With the introduction of advanced LLMs such as GPT-3, GPT-4, Gopher, PALM, LAMDA, and more, the future of natural language processing looks promising. These models will continue to improve the capability of machines to understand and process human language and will have far-reaching impacts on many industries and fields of research.
Thanks for reading!
