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Computer Science > Computation and Language

arXiv:2306.16636 (cs)
[Submitted on 29 Jun 2023]

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

Authors:Tianwen Wei, Jian Luan, Wei Liu, Shuang Dong, Bin Wang
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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.
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2306.16636 [cs.CL]
  (or arXiv:2306.16636v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2306.16636

Submission history

From: Tianwen Wei [view email]
[v1] Thu, 29 Jun 2023 02:19:50 UTC (401 KB)
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