Summary of Entropy Law: the Story Behind Data Compression and Llm Performance, by Mingjia Yin et al.
Entropy Law: The Story Behind Data Compression and LLM Performance
by Mingjia Yin, Chuhan Wu, Yufei Wang, Hao Wang, Wei Guo, Yasheng Wang, Yong Liu, Ruiming Tang, Defu Lian, Enhong Chen
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 research paper presents a novel approach to improving the performance of large language models (LLMs) by selecting data subsets that are more effective in teaching LLMs. The authors propose a method called ZIP, which prioritizes data subsets with low compression ratios, reflecting the information redundancy of a dataset and the mastery of inherent knowledge encoded in it. They demonstrate that LLM performance is negatively correlated to the compression ratio of training data, leading to a lower training loss. The proposed approach is shown to be efficient, universal, and applicable across different LLM backbones and alignment stages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how we can train better language models by choosing just the right data. Right now, most people focus on making sure each piece of data is good quality, but they don’t think about how all those pieces fit together. The researchers found that even if each piece of data is great on its own, some combinations might be better than others for teaching language models. They discovered a “law” that connects the performance of language models to the way we compress and use their training data. This law helps us create a new way to select data, called ZIP, which picks out the most important pieces and makes sure they’re diverse enough. The team tested this approach with different types of language models and showed that it works well. |
Keywords
* Artificial intelligence * Alignment
