Summary of Mustard: Mastering Uniform Synthesis Of Theorem and Proof Data, by Yinya Huang et al.
MUSTARD: Mastering Uniform Synthesis of Theorem and Proof Data
by Yinya Huang, Xiaohan Lin, Zhengying Liu, Qingxing Cao, Huajian Xin, Haiming Wang, Zhenguo Li, Linqi Song, Xiaodan Liang
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Formal Languages and Automata Theory (cs.FL); Machine Learning (cs.LG); Programming Languages (cs.PL)
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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 introduces MUSTARD, a novel framework for generating high-quality and diverse mathematical theorem and proof data. MUSTARD synthesizes data by sampling mathematical concept seeds, prompting generative language models to obtain problems and solutions, and filtering valid proofs using proof assistants. The proposed benchmark, MUSTARDSAUCE, contains 5,866 validated data points, including informal statements, proofs, and formal proofs. The paper demonstrates the effectiveness of MUSTARD in generating high-quality step-by-step data and fine-tuning smaller language models for automated theorem proving and math word problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Recent research has led to significant advancements in large language models (LLMs) for tasks like mathematical reasoning and theorem proving. To explore these abilities, researchers have been working on challenges in these areas. One key issue is that previous studies requiring step-wise annotation have shown effectiveness but require heavy labor. This paper addresses this gap by introducing a new framework called MUSTARD. It uses three stages to synthesize data: sampling concept seeds, prompting language models for problems and solutions, and filtering valid proofs with proof assistants. The result is a benchmark dataset of 5,866 validated data points that can be used to fine-tune smaller language models. |
Keywords
* Artificial intelligence * Fine tuning * Prompting
