Top five large-scale language models (LLMs) in 2023
As of 2023, artificial intelligence is taking the world by storm. It has become a popular topic of discussion, attracting the attention of millions of people, not just technology experts and researchers, but also individuals from diverse backgrounds. One of the reasons for the heightened enthusiasm for artificial intelligence is its capabilities in various forms of domains that humans have dealt with for years, including language. Language is an integral part of human life, helping us communicate, understand the things around us, and even help us think. However, AI is now more capable of processing language at or above human levels. This is due to advances in natural language processing (NLP) and large language models (LLMs), one of which is behind ChatGPT, the great initiative of San Francisco-based startup OpenAI. However, OpenAI became one of the companies that successfully brought its LLM technology to the public. There are many large and small companies building many large language models of this type. In this article, we will give an overview of large language models as well as some of the advanced LLMs in the world, to be precise, we will discuss 5 of them. It is important to note that these lists of LLMs are compiled through research from various sources and are not based on rankings.
The essence of large language models
In recent years, natural language processing (NLP) has developed rapidly due to the ability of computers to store and process large amounts of natural text data. Applications of NLP can be seen in various technologies that we have used for decades, such as speech recognition, chatbots, etc. Since the advent of machine learning, scientists have begun to combine NLP with state-of-the-art machine learning techniques to process text more efficiently. However, recently NLP has become more popular due to the emergence of powerful large language models (LLMs).
So what are large language models and why are they so powerful? A language model is basically a special type of machine learning model that can learn, understand, and process human language efficiently. By learning from a data set containing text, a language model can predict the next word or sentence with a high degree of accuracy. But when they get bigger, they become more interesting and special. LLMs are trained on very large text data sets (millions or billions of text data) and require significant computing power. In comparison, if a language model is like a garden, then a large language model is like a dense forest.
How do LLMs work?
As we said, LLMs are machine learning models that can do many things with text, such as translating one language to another, generating language, answering questions, etc. But how do they do it? The possibility of building LLMs comes from a special type of neural network architecture proposed by Google researchers called Transformer.
Transformer is a type of neural network specifically designed to perform magic on text data. They are great for scaling efficiently and can be trained on very large text corpora, even billions or even trillions of text! Additionally, transformers can be trained much faster than other types of neural networks, such as recurrent neural networks. What’s more interesting is that Transformer can be trained in parallel, which means that multiple computing resources (such as CPU or GPU) can be utilized simultaneously to accelerate the learning process, while RNN can only process data sequentially.
Another interesting feature of the transformer model is the self-attention technology. This mechanism allows Transformer to learn the underlying meaning of language, rather than just generating randomly relevant text piece by piece. Because of this ability, today's language models don't just output text one by one, but they learn the actual meaning of the language (just like humans) by being fed large amounts of text data, including syntax, semantics, and context.
The invention of the Transformer model developed by Google has achieved significant achievements in the fields of artificial intelligence and natural language processing (NLP). With the help of this Transformer model, many large, small and even startup companies are building LLMs and using them for different purposes such as technical chat support, voice assistants, content generation, chatbots, and more. We cannot discuss every LLMs that exists today because there are many of them. So, now, let us discuss the 5 most advanced LLMs existing in the world in 2023, these LLMs are as follows:
1、GPT-4（OpenAI）
GPT-4, which stands for Generative Pre-trained Transformer-4, is OpenAI’s most advanced and highly complex large-scale language model. It is the fourth generation language model released on March 14, 2023, following the successful launch of ChatGPT equipped with GPT-3.5. It is equipped with top-notch reasoning and creative capabilities that exceed people's imagination. GPT-4 is a massive neural network containing a staggering 1 trillion parameters and trained on a large text dataset containing code from a variety of programming languages. In addition, GPT-4 is not only proficient in text processing, but also demonstrates the ability to process visual data, including images. With its ability to understand and generate content from textual and visual input, GPT-4 can be considered a powerful multi-modal artificial intelligence that bridges the linguistic and visual domains.
Another interesting feature of GPT-4 is the amount of data it can handle in a single request. OpenAI's predecessor language model could handle up to 3,000 tokens in a single request, but GPT-4 can handle up to 25,000 tokens in a single request. This is so large that you can actually ask GPT-4 to summarize an entire 10-page PDF in one operation.
Even more interestingly, OpenAI scientists and researchers say GPT-4 offers a glimpse of artificial general intelligence (AGI) that many scientists believe may be unlikely to be achieved in the next 40 or 50 years. However, according to an OpenAI blog post, GPT-4 is not a perfect system and can suffer from hallucinations and incorrect answers.
2、GPT-3（OpenAI）
GPT-3, which stands for Generative Pre-trained Transformer 3, is another impressive Transformer-based language model. Launched by OpenAI on June 11, 2020, it remains one of the most advanced LLMs on the market in 2023. one. It uses advanced deep learning techniques such as Transformer and attention mechanisms to process and generate text that is indistinguishable from human-written text.
Essentially, GPT-3 is huge, with approximately 175 billion parameters, uses advanced natural language processing (NLP), and is built on multi-gigabytes of data from various sources including Wikipedia, WebText2, books, articles, and code. were trained on the text dataset. This complexity makes GPT-3 superior in language processing, including text generation, language translation, and question answering. Additionally, GPT-3 was extensively trained on much of GitHub, giving it expertise in a broad range of programming languages and concepts.
After the success of GPT-3, the company has once again launched an enhanced version of GPT-3 called GPT-3.5, which is powering ChatGPT.
3、Gopher（DeepMind）
Gopher is an AI language model developed by Google DeepMind that is specially trained for tasks such as reading comprehension, fact-checking, understanding toxic language, and logic and common sense tasks.
Researchers at DeepMind developed a range of language models, ranging from 44 million parameters to 280 billion parameters, which were trained on large amounts of text from a variety of sources. Among these language models, the 280 billion parameter model shows stronger capabilities in language understanding and generation, which they call Gopher. In their research, they found that Gopher surpassed existing language models and achieved human-level expertise in a variety of tasks, including massive multi-task language understanding (MMLU), which is used to measure large language models. New benchmarks in the ability to understand and respond to a variety of language tasks. This study shows that Gopher outperforms other language models, including GPT-3, in fields such as mathematics, science, technology, humanities, and medicine.
Gopher is designed to excel in conversation-based interactions, allowing it to explain even complex topics with chat-like responses. If you visit their company blog, you can see examples of Gopher explaining cell biology in very simple terms.
4、PaLM（Google）
PaLM, which stands for Pathways Language Model, is an advanced language model from Google designed to summarize multiple fields within a single model. It uses the Pathways architecture to better understand language and eliminates some limitations of existing language models (such as domain-specificity, singularity, etc.). Pathways is a relatively new neural network architecture that continues to improve through research conducted at Google. Pathways enable AI systems to excel in multiple areas rather than just focusing on a single set of tasks. It also enables AI models to be multimodal, meaning they can process and understand information from different modalities (such as text, images, and audio) simultaneously.
PaLM is a Transformer-based language model with 540 billion parameters. It shows excellent performance in various fields such as language understanding, question answering, arithmetic, coding, language translation, logical reasoning, dialogue, etc. Even more interestingly, researchers at Google integrated their PaLM model into a real-world robot by adding sensory information and robot gestures and control. The robot can perform a variety of tasks through its PaLM brain, including having meaningful conversations with humans, understanding and responding to spoken commands, navigating autonomously, manipulating objects using robotic arms, and performing a variety of real-world tasks.
PaLM is one of the research areas Google is actively pursuing, and the company is developing new, high-performance versions of PaLM. In fact, they recently launched PaLM-2, a model with impressive reasoning, encoding, and multilingual capabilities.
5. LaMDA (Google)
LaMDA, which stands for Language Model for Dialogue Applications, is another language model developed by Google in research conducted in early 2020. Unlike other language models, LaMDA is mainly trained on dialogue-based text, which is very beneficial for dialogue. As a result of being trained on conversations, LaMDA demonstrated exceptional skill at conducting meaningful conversations at a human level. This ability of LaMDA is so outstanding that a former Google employee even believed that LaMDA was thoughtful.
LaMDA is based on advanced NLP technology and adopts a Transformer-based neural network model. According to researchers at Google, combining Transformer-based models with conversation has the potential to make large language models better at conducting human-level conversations and even eventually learn to talk about almost anything. Additionally, after training on large amounts of dialogue text, LaMDA can be fine-tuned using reinforcement learning to make it more difficult to distinguish AI in dialogue-based tasks.
In February 2023, Google integrated its latest version of LaMDA into a chatbot called Bard, which is now available globally. However, Google says they have replaced the technology behind Bard from LaMDA to PaLM-2.
Other nominations worth mentioning
LLaMA (Meta AI): LLaMA (Large Language Model Meta AI) is a series of open source LLMs developed by Meta (formerly Facebook). Among them, LLaMA 1 was released in February 2023 and is considered one of the best open source language models that can be used for a variety of NLP tasks without paying anything, except that you may need to run a GPU at home. The first version of LLaMA 1 includes 7, 1.3, 3.3 and 6.5 billion parameter models. Among them, researchers at Meta found that a model with 1.3 billion parameters performed better than GPT-3 (175 billion) on most NLP tasks. The 6.5 billion model performs better and may compete with Google's PaLM model.
Claude (Anthropic): Claude is a large language model similar to GPT-3 developed by Anthropic. Unlike other LLMs, Claude's training dataset is primarily manually created by human authors rather than automatically collected data. This allows Claude to better understand and generate high-quality text. In addition, Anthropic said that Claude is not a general-purpose large-scale language model, but a model that uses humans as a reference, and its goal is to provide help and guidance when writing, not just generate text.
Summarize
Today, with the rapid development of artificial intelligence, large language models (LLMs) have become a hot topic. They have achieved great success in the field of natural language processing (NLP) and have been widely used in a variety of applications, from text generation to question answering to conversational AI. We see many companies continually launching increasingly powerful LLMs that surpass previous records in language understanding and generation. In 2023, state-of-the-art LLMs like GPT-4, GPT-3, Gopher, PaLM, and LaMDA demonstrated significant progress in AI understanding and processing human language. However, these models still face challenges, such as hallucinations, incorrect answers, etc., but they still provide huge opportunities for scientific research, business, and innovation. As technology continues to evolve, LLMs may bring innovation in more fields and have a positive impact on human life.
Blog reference:
https://www.pycodemates.com/2023/06/large-language-models-overview-and-types-of-llm.html