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arXiv · 2505.15623

Can LLMs $\textit{understand}$ Math? -- Exploring the Pitfalls in Mathematical Reasoning

Tiasa Singha Roy·Aditeya Baral·Ayush Rajesh Jhaveri·Yusuf Baig
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Abstract

Large language models (LLMs) demonstrate considerable potential in various natural language tasks but face significant challenges in mathematical reasoning, particularly in executing precise, multi-step logic. However, current evaluation frameworks judge their performance solely based on accuracy, which only accounts for the final answer. This study explores these pitfalls by employing a novel evaluation framework. We propose an evaluation metric called the MAPLE score, which holistically quantifies reasoning misalignment by integrating error rates, redundancy, and validity.

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Tiasa Singha Roy, Aditeya Baral, Ayush Rajesh Jhaveri, Yusuf Baig. 2025-05-21. Can LLMs $\textit{understand}$ Math? -- Exploring the Pitfalls in Mathematical Reasoning. https://arxiv.org/abs/2505.15623

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