Neuro-Symbolic Data Generation for Math Reasoning

• URL: http://arxiv.org/abs/2412.04857v1
• Date: Fri, 06 Dec 2024 08:49:49 GMT
• Title: Neuro-Symbolic Data Generation for Math Reasoning
• Authors: Zenan Li, Zhi Zhou, Yuan Yao, Yu-Feng Li, Chun Cao, Fan Yang, Xian Zhang, Xiaoxing Ma,
• Abstract summary: We develop an automated method for generating high-quality, supervised mathematical datasets.<n>The method carefully mutates existing math problems, ensuring both diversity and validity of the newly generated problems.<n> Empirical experiments demonstrate the high quality of data generated by the proposed method, and that the LLMs, specifically LLaMA-2 and Mistral, surpass their state-of-the-art counterparts.
• Score: 47.00099724151703
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: A critical question about Large Language Models (LLMs) is whether their apparent deficiency in mathematical reasoning is inherent, or merely a result of insufficient exposure to high-quality mathematical data. To explore this, we developed an automated method for generating high-quality, supervised mathematical datasets. The method carefully mutates existing math problems, ensuring both diversity and validity of the newly generated problems. This is achieved by a neuro-symbolic data generation framework combining the intuitive informalization strengths of LLMs, and the precise symbolic reasoning of math solvers along with projected Markov chain Monte Carlo sampling in the highly-irregular symbolic space. Empirical experiments demonstrate the high quality of data generated by the proposed method, and that the LLMs, specifically LLaMA-2 and Mistral, when realigned with the generated data, surpass their state-of-the-art counterparts.

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