Learning Mathematical Rules with Large Language Models

• URL: http://arxiv.org/abs/2410.16973v3
• Date: Fri, 25 Oct 2024 13:28:52 GMT
• Title: Learning Mathematical Rules with Large Language Models
• Authors: Antoine Gorceix, Bastien Le Chenadec, Ahmad Rammal, Nelson Vadori, Manuela Veloso,
• Abstract summary: We study the ability of large language models to learn specific mathematical rules such as distributivity or simplifying equations. We present an empirical analysis of their ability to generalize these rules, as well as to reuse them in the context of word problems.
• Score: 10.285317818397298
• License:
• Abstract: In this paper, we study the ability of large language models to learn specific mathematical rules such as distributivity or simplifying equations. We present an empirical analysis of their ability to generalize these rules, as well as to reuse them in the context of word problems. For this purpose, we provide a rigorous methodology to build synthetic data incorporating such rules, and perform fine-tuning of large language models on such data. Our experiments show that our model can learn and generalize these rules to some extent, as well as suitably reuse them in the context of word problems.

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