Summary of Unlocking Tokens As Data Points For Generalization Bounds on Larger Language Models, by Sanae Lotfi et al.
Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models
by Sanae Lotfi, Yilun Kuang, Brandon Amos, Micah Goldblum, Marc Finzi, Andrew Gordon Wilson
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle the challenge of deriving meaningful compression-based generalization bounds for large language models (LLMs) with billions of parameters. Current methods fail to provide tight bounds due to restrictive compression techniques, which result in low-quality text generation. The authors propose a novel approach using martingales to derive bounds that take advantage of the vast number of tokens in LLM training sets, rather than just IID documents. They demonstrate non-vacuous generalization bounds for LLaMA2-70B, a large-scale model capable of generating high-quality text. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can predict the next token in a sequence with ease. But how do we measure their performance? Researchers have developed ways to make these models smaller without losing their ability to generate good text. However, these methods only work well for small models and don’t apply to the very large ones used in practice. In this paper, scientists find a new way to measure the performance of large language models using special mathematical tools called martingales. This approach works even when the model is very large and can generate high-quality text. |
Keywords
» Artificial intelligence » Generalization » Text generation » Token
