Summary of Large Language Models: a Survey, by Shervin Minaee et al.
Large Language Models: A Survey
by Shervin Minaee, Tomas Mikolov, Narjes Nikzad, Meysam Chenaghlu, Richard Socher, Xavier Amatriain, Jianfeng Gao
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This abstract presents an overview of Large Language Models (LLMs), which have gained significant attention since the release of ChatGPT in November 2022. LLMs are trained on massive amounts of text data to acquire general-purpose language understanding and generation capabilities, as predicted by scaling laws. The paper reviews prominent LLM families, such as GPT, LLaMA, and PaLM, discussing their characteristics, contributions, and limitations. It also surveys techniques for building and augmenting LLMs, popular datasets, evaluation metrics, and compares the performance of several popular LLMs on representative benchmarks. Finally, the paper discusses open challenges and future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are special computers that can understand and generate human-like language. They’re really good at doing this because they were trained on a huge amount of text data. Since ChatGPT was released in November 2022, people have been talking about LLMs a lot. This paper looks at some popular types of LLMs, like GPT, LLaMA, and PaLM, and talks about what makes them special and how they work. It also explains how scientists build and test these models using different techniques, datasets, and metrics. |
Keywords
» Artificial intelligence » Attention » Gpt » Language understanding » Llama » Palm » Scaling laws
