Summary of Mathcoder2: Better Math Reasoning From Continued Pretraining on Model-translated Mathematical Code, by Zimu Lu et al.
MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code
by Zimu Lu, Aojun Zhou, Ke Wang, Houxing Ren, Weikang Shi, Junting Pan, Mingjie Zhan, Hongsheng Li
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 The proposed method generates mathematical code accompanied with reasoning steps for continued pretraining, enhancing the mathematical reasoning abilities of large language models. This is achieved by constructing a high-quality dataset through web data, code, math textbooks, and synthetic data, then extracting LaTeX expressions, conditions, and results to generate corresponding code. The approach leads to a 19.2B-token pretraining corpus named MathCode-Pile, which improves the mathematical abilities of popular base models like BERT and RoBERTa. The open-sourced code is released at https://github.com/mathllm/MathCoder2. This research can be used for training AI models that excel in math-related tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to teach large language models math skills. They collect lots of math-related data from the internet and textbooks, then use it to create code that shows how to solve math problems step by step. This helps the models learn math better. The researchers also trained popular AI models like BERT using this new math training method and got great results. |
Keywords
» Artificial intelligence » Bert » Pretraining » Synthetic data » Token
