CMATH: Can Your Language Model Pass Chinese Elementary School Math Test?

• URL: http://arxiv.org/abs/2306.16636v1
• Date: Thu, 29 Jun 2023 02:19:50 GMT
• Title: CMATH: Can Your Language Model Pass Chinese Elementary School Math Test?
• Authors: Tianwen Wei, Jian Luan, Wei Liu, Shuang Dong, Bin Wang
• Abstract summary: We present the Chinese Elementary School Math Word Problems dataset, comprising 1.7k elementary school-level math word problems with detailed annotations. This dataset aims to provide a benchmark tool for assessing the abilities of popular large language models (LLMs) We evaluate a variety of popular LLMs, including both commercial and open-source options, and discover that only GPT-4 achieves success (accuracy $geq$ 60%) across all six elementary school grades.
• Score: 15.53530547827583
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present the Chinese Elementary School Math Word Problems (CMATH) dataset, comprising 1.7k elementary school-level math word problems with detailed annotations, source from actual Chinese workbooks and exams. This dataset aims to provide a benchmark tool for assessing the following question: to what grade level of elementary school math do the abilities of popular large language models (LLMs) correspond? We evaluate a variety of popular LLMs, including both commercial and open-source options, and discover that only GPT-4 achieves success (accuracy $\geq$ 60\%) across all six elementary school grades, while other models falter at different grade levels. Furthermore, we assess the robustness of several top-performing LLMs by augmenting the original problems in the CMATH dataset with distracting information. Our findings reveal that GPT-4 is able to maintains robustness, while other model fail. We anticipate that our study will expose limitations in LLMs' arithmetic and reasoning capabilities, and promote their ongoing development and advancement.

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