Evaluating Mathematical Reasoning of Large Language Models: A Focus on Error Identification and Correction

• URL: http://arxiv.org/abs/2406.00755v1
• Date: Sun, 2 Jun 2024 14:16:24 GMT
• Title: Evaluating Mathematical Reasoning of Large Language Models: A Focus on Error Identification and Correction
• Authors: Xiaoyuan Li, Wenjie Wang, Moxin Li, Junrong Guo, Yang Zhang, Fuli Feng,
• Abstract summary: Existing evaluations of Large Language Models (LLMs) focus on problem-solving from the examinee perspective. We define four evaluation tasks for error identification and correction along with a new dataset with annotated error types and steps. Our principal findings indicate that GPT-4 outperforms all models, while open-source model LLaMA-2-7B demonstrates comparable abilities to closed-source models GPT-3.5 and Gemini Pro.
• Score: 35.01097297297534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid advancement of Large Language Models (LLMs) in the realm of mathematical reasoning necessitates comprehensive evaluations to gauge progress and inspire future directions. Existing assessments predominantly focus on problem-solving from the examinee perspective, overlooking a dual perspective of examiner regarding error identification and correction. From the examiner perspective, we define four evaluation tasks for error identification and correction along with a new dataset with annotated error types and steps. We also design diverse prompts to thoroughly evaluate eleven representative LLMs. Our principal findings indicate that GPT-4 outperforms all models, while open-source model LLaMA-2-7B demonstrates comparable abilities to closed-source models GPT-3.5 and Gemini Pro. Notably, calculation error proves the most challenging error type. Moreover, prompting LLMs with the error types can improve the average correction accuracy by 47.9\%. These results reveal potential directions for developing the mathematical reasoning abilities of LLMs. Our code and dataset is available on https://github.com/LittleCirc1e/EIC.

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