Summary of Physics Of Language Models: Part 2.2, How to Learn From Mistakes on Grade-school Math Problems, by Tian Ye et al.
Physics of Language Models: Part 2.2, How to Learn From Mistakes on Grade-School Math Problems
by Tian Ye, Zicheng Xu, Yuanzhi Li, Zeyuan Allen-Zhu
First submitted to arxiv on: 29 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Recent advancements in language models have led to impressive performance in solving reasoning tasks. However, even top-performing models still make mistakes occasionally. This paper builds upon the idea of using pre-trained language models to “self-correct” errors through multi-round prompting, but takes a different approach by incorporating error-correction data directly into the pretraining stage. The study uses a synthetic math dataset and demonstrates promising results, showing that this type of pretrain data can improve reasoning accuracy without requiring multi-round prompting. The paper also explores various details, including differences with beam search, preparation methods, masking requirements, and deferring error data to fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a super smart computer program that can solve math problems really well, but sometimes makes mistakes. Scientists are trying to make this program even better by letting it learn from its own mistakes. This paper is about a new way to teach the program by giving it wrong answers and then correcting them. The results show that this method helps the program be more accurate in solving math problems without needing multiple attempts. It’s like teaching a student by showing them what not to do, so they can learn from their mistakes. |
Keywords
» Artificial intelligence » Fine tuning » Pretraining » Prompting
