--- license: cc-by-4.0 language: - zh tags: - mathematics size_categories: - 1K= 60%) across all six elementary school grades, while other models falter at different grade levels. Furthermore, we assess the robustness of LLMs by augmenting the original problems in the CMATH dataset with distracting information. Our findings reveal that GPT-4 is the sole model that maintains robustness, further distinguishing its performance from competing models. We anticipate that our CMATH dataset will expose limitations in LLMs' capabilities and promote their ongoing development and advancement. ## Datasets ### cmath_dev Initial release of 600 examples from CMATH dataset, with 100 problems from each elementary school grade. We will release the remaining portion of the dataset by the end of the year. #### Examples and Annotations ![Examples](assets/example1.png) #### Evaluation Results ![Model Performance](assets/plot1.png) ### distractor To assess the robustness of LLMs against "irrelevant" information, we manually created a small ``distractor dataset'' comprising 60 examples, 10 for each grade level. Each example consists of an original problem and five associated problems augmented with 1 ~ 5 piece(s) of irrelevant information which we refer to as distractor(s). #### Examples ![Examples](assets/example2.png) #### Evaluation Results ![Model Performance](assets/plot2.png) ## Script We provide a script `eval.py` that implements automated evaluation. ## License CC BY 4.0 ## Citation ``` @misc{wei2023cmath, title={CMATH: Can Your Language Model Pass Chinese Elementary School Math Test?}, author={Tianwen Wei and Jian Luan and Wei Liu and Shuang Dong and Bin Wang}, year={2023}, eprint={2306.16636}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` Visit our git [repository](https://github.com/XiaoMi/cmath) for more details. You may also read our [paper](https://arxiv.org/abs/2306.16636).