Introduction

LLM stands for “Large Language Model.” An LLM is an artificial intelligence model based on deep learning (machine learning) that can generate, understand, and analyze natural language text in a highly advanced manner.

How an LLM Works

An LLM is trained on immense amounts of textual data to learn the statistical patterns and relationships between words and phrases. With its millions or even billions of parameters, it can probabilistically predict the most relevant continuation of a given input word sequence.[1][3][4] However, contrary to popular belief, an LLM does not actually “understand” the meaning of the text, it simply performs highly advanced statistical predictions.[3]

Use Cases for LLMs

LLMs have multiple use cases, such as text generation, automatic summarization, translation, question-answering, computer programming, and more. They are used in various fields: education, technology, healthcare, customer service, marketing, legal, etc.[1]

LLMs are notably used in chat applications such as ChatGPT, Gemini (formerly Bard), Copilot, etc.[3][4]

ChatGPT is not an LLM itself but an application that interacts with the GPT series LLMs developed by OpenAI.

Limitations of LLMs

Despite their impressive performance, LLMs have significant limitations. They can produce hallucinations and fabricate erroneous information. They also pose risks in terms of security, data privacy, and biases.[1][4] Their reliability heavily depends on the quality of the training data and the input data (prompt) provided.[4]

History of LLMs

Large Language Models (LLMs) have a relatively recent history, but their roots go back to the 1950s with the early work on neural networks and natural language processing. Here is a brief overview of their evolution:

1950s-1960s: Early Steps

– 1950: Pioneer experiments with neural networks for natural language processing.
– 1966: Creation of ELIZA, one of the first chatbots, by Joseph Weizenbaum at MIT, laying the foundation for natural language processing.[5][6]

1990s-2000s: Emergence of Neural Language Models

– 1990s: Development of the first neural network-based language models with modest performance.[5]

2010s: Key Technical Advances

– 2017: Introduction of transformers and self-attention mechanisms, enabling better capture of relationships in sequential data.[5][8][9]
– 2018: Launch of the first large models like BERT (Google) and GPT-1 (OpenAI), leveraging transformers.[7][8]

2020s: Rise of LLMs

– 2020: GPT-3 (OpenAI) marks a turning point with its 175 billion parameters and enhanced capabilities.[8]
– 2021-2023: Emergence of many other LLMs like PaLM (Google), LLaMA (Meta), Claude (Anthropic), reaching hundreds of billions of parameters.[5][8]
– November 2022: Launch of ChatGPT (OpenAI), demonstrating the impressive capabilities of LLMs for natural dialogue.[7]

The rapid development of LLMs in recent years is made possible by advances in computing power, massive datasets, and efficient training techniques.[5][6] Their impact is now considerable in various fields such as text generation, dialogue, programming, etc.[7][8]

ChatGPT: The Technological Breakthrough Bulldozer

One of the first language models to attract attention was BERT. At the time, Google had invested several tens of thousands of dollars for BERT’s initial training.[10] At the time, this was already a significant gamble and investment. The first returns of BERT were excellent and a clear improvement over previous natural language processing (NLP) models, even though it was far from being capable of handling discussions on a multitude of topics as it was with the introduction of ChatGPT in November 2022.

But what was OpenAI’s recipe for pushing the limits of such models? The answer is relatively simple: money. Until then, transformer models such as BERT were mainly used and studied by researchers or data scientists working on innovative topics. These user profiles primarily focused on technical or algorithmic solutions, including exploring different model training strategies.

OpenAI applied a different strategy based on a simple hypothesis: training models with many, many more parameters would yield more powerful language models. Thus, GPT-3 emerged as a large language model (LLM). The secret to improving language model performance was to train them with more parameters, which meant training them longer and/or on more powerful servers. This implied accepting to spend astronomical sums (several million dollars) for the training of a single LLM.

Although the exact amount is not known, some have estimated that OpenAI’s initial training of GPT-3 probably cost tens of millions of dollars, or even more, due to the enormous computational resources required with its 175 billion parameters.[8][11][12] As evidenced by the success of ChatGPT, their hypothesis was confirmed, and the gamble paid off.

Comparison: GPT-4 vs LLaMA vs Mistral

Since GPT-3’s breakthrough, several LLMs have emerged. The three main ones (those that give results closest to human interaction) are GPT-4, LLaMA, and Mistral.[13][14][15]

GPT-4 (OpenAI)

GPT-4 is considered the benchmark model in terms of performance. With its 1.76 trillion parameters, it far surpasses other LLMs in benchmarks like Flask, which evaluates logic, knowledge, problem-solving, and user alignment. GPT-4 achieves the highest scores on these criteria.[13][15]

LLaMA (Meta)

LLaMA is a popular open-source LLM available in several versions with up to 70 billion parameters. Its performance is significantly lower than GPT-4 according to benchmarks, but it remains a powerful model, especially in its 70B version.[13][15]

Mistral (Mistral AI)

Mistral AI has recently launched Mistral Large, a multilingual model (English, French, German, etc.) that approaches GPT-4’s performance in some benchmarks like MMLU. Its precise capabilities are not detailed, but it seems to rival GPT-4 for some complex tasks. Mistral also has lighter versions such as Mistral Medium and Small.[15]

In summary, GPT-4 remains the most performant LLM overall, but Mistral Large comes close in some aspects, while LLaMA is a good open-source model, though less powerful.[13][14][15][16]

Conclusion

Large Language Models (LLMs) represent a significant advancement in the field of artificial intelligence and natural language processing. Their ability to generate, understand, and analyze text in an extremely sophisticated way has opened new possibilities in various sectors such as education, technology, healthcare, and more. However, it is crucial to recognize their limitations, notably the risks related to security, data privacy, and potential biases.

The rapid evolution of LLMs, marked by milestones like BERT, GPT-3, and now GPT-4, has been made possible by considerable investments in computational power and training data. The rise of models like LLaMA and Mistral also shows growing diversification and competition in this domain. Despite everything, GPT-4 currently remains the benchmark in terms of performance, though other models are starting to approach for specific tasks.