Summary of Neuro-symbolic Data Generation For Math Reasoning, by Zenan Li et al.
Neuro-Symbolic Data Generation for Math Reasoning
by Zenan Li, Zhi Zhou, Yuan Yao, Yu-Feng Li, Chun Cao, Fan Yang, Xian Zhang, Xiaoxing Ma
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 paper explores whether Large Language Models’ (LLMs) mathematical reasoning limitations are inherent or due to insufficient exposure to high-quality mathematical data. The authors developed an automated method for generating diverse and valid mathematical datasets by combining LLMs with math solvers and Markov chain Monte Carlo sampling. This approach generates high-quality data that improves LLM performance, surpassing state-of-the-art models like LLaMA-2 and Mistral. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper investigates whether Large Language Models’ (LLMs) poor mathematical skills are due to their training or a lack of good math problems. To answer this question, the researchers created an automatic way to generate many new math problems that are similar but not identical to existing ones. They did this by combining two different approaches: using large language models to make math problems more understandable and using math solvers to ensure the problems are correct. This method creates a lot of high-quality math problems that help LLMs learn and improve, making them better than other models like LLaMA-2 and Mistral. |
Keywords
» Artificial intelligence » Llama
