大模型能学会并应用数学规则解决实际问题。
Learning Mathematical Rules with Large Language Models
- 用合成数据训练模型学习分配律等数学规则。
- 模型在文字题中能较好复用所学规则，具备一定泛化能力。
- 适合研究大模型数学推理能力的学者参考。
本文研究大语言模型学习特定数学规则（如分配律、方程简化）的能力，并评估其在文字题中复用这些规则的泛化表现。为此，我们设计了一套严谨的方法构建包含这些规则的合成数据，并对大语言模型进行微调。实验表明，模型能够在一定程度上学会并泛化这些规则，同时在文字题语境中适当地复用它们。
原文摘要 · Abstract (English)
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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