Recently, I read a paper published last month titled "Large Language Models: A Survey." https://arxiv.org/abs/2402.06196
This article explores the astonishing performance of large language models (LLMs) in various natural language processing tasks since the release of ChatGPT in November 2022, examining how LLMs are changing the way we interact with technology.
Article abstract
It reviews several of the most prominent large language models, including the three major popular series GPT, LLaMA, and PaLM, discussing their characteristics, contributions, and limitations. It outlines the technical approaches for building and enhancing large language models, surveys popular datasets prepared for training, fine-tuning, and evaluating LLMs, reviews widely used evaluation metrics, and compares the performance of several popular models across a range of representative benchmarks. By discussing future research directions and challenges, it provides readers with a development blueprint for the field of large language model research.
You can read the original text, but I have extracted various tables from it and visualized them to help you understand.
1. Classification of LLMs
By parameter size By category By openness Nativity
2. Overview of LLMs
Provides a brief introduction to different series of LLMs, including their classification, parameter size, and training datasets. The situation of the three giants: OpenAI, Meta, and Google
3. Capability map of LLMs
Explores the impressive performance of LLMs in various natural language processing tasks, such as understanding, multilingualism, knowledge, code, reasoning, and dialogue.
4. Scaling Law
As the number of model parameters increases, their ability to understand and utilize contextual information also improves.
5. Milestones of LLMs
Starting from the original Transformer, this concept was like a breath of fresh air that completely transformed the landscape of natural language processing. It was not just a model but a completely new approach that laid the foundation for subsequent developments. The emergence of BERT significantly enhanced the machine's ability to understand language by deeply comprehending contextual meaning, greatly improving the model's understanding capability. GPT-1 was another milestone, initiating the era of generative pre-trained models and laying the foundation for the subsequent GPT series.
Over time, we have witnessed more innovations, each step continuously expanding the boundaries of large language models. In this process, some models were not only technological breakthroughs but also represented entirely new explorations of problem-solving methods. Others focused on methodological innovation; although they may not have had enormous parameter scales, they played an indispensable role in advancing the development of language models.
6. How Transformers work
Introduces the basic principles of the Transformer model as a foundation for understanding LLMs.
7. How DPO works
Introduces the working principle of DPO (Direct Preference Optimization) and its application in LLMs. This has been shared before:
8. Technical details of LoRA (Low-Rank Adaptation) reparameterization
In-depth discussion of LoRA technology and how it optimizes the parameterization process of LLMs.
9. Construction process of LLMs
Explains the basic steps for constructing large language models, including data collection, model design, and training.
10. Usage and enhancement of LLMs
Usage methods and advantages of RAG models Application scenarios and methods for combining knowledge graphs (KG) with LLMs Enhancing the capabilities of LLMs using tools and platforms like HuggingGPT Technical details of agent-based conversational information retrieval Application and importance of datasets in LLMs