MATHSENSEI: A Tool-Augmented Large Language Model for Mathematical Reasoning

1Indian Institute of Technology Kharagpur
2Rakuten Group, Inc. Rakuten Institute of Technology India

MathSensei helps in Solving Complex Mathematical Problems using a Tool-Augmented LLM

Abstract

Tool-augmented Large Language Models (TALMs) are known to enhance the skillset of large language models (LLMs), thereby, leading to their improved reasoning abilities across many tasks. While, TALMs have been successfully employed in different question-answering benchmarks, their efficacy on complex mathematical reasoning benchmarks, and the potential complementary benefits offered by tools for knowledge retrieval and mathematical equation solving are open research questions. In this work, we present MathSensei, a tool-augmented large language model for mathematical reasoning. We study the complementary benefits of the tools - knowledge retriever (Bing Web Search), program generator + executor (Python), and symbolic equation solver (Wolfram-Alpha API) through evaluations on mathematical reasoning datasets. We perform exhaustive ablations on MATH, a popular dataset for evaluating mathematical reasoning on diverse mathematical disciplines. We also conduct experiments involving well-known tool planners to study the impact of tool sequencing on the model performance.

MathSensei achieves 13.5% better accuracy over gpt-3.5-turbo with Chain-of-Thought on the MATH dataset. We further observe that TALMs are not as effective for simpler math word problems (in GSM-8K), and the benefit increases as the complexity and required knowledge increases (progressively over AQuA, MMLU-Math, and higher level complex questions in MATH).

Method

An end-to-end workflow of MathSensei on the compositional setting from the MATH dataset. The final answer is higlighted in green font.

BibTeX

@article{Das:2024:MathSensei,
      title={MATHSENSEI: A Tool-Augmented Large Language Model for Mathematical Reasoning}, 
      author={Debrup Das and Debopriyo Banerjee and Somak Aditya and Ashish Kulkarni},
      year={2024},
      eprint={2402.17231},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
    }