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license: mit |
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--- |
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## DocMath-Eval |
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[**🌐 Homepage**](https://docmath-eval.github.io/) | [**🤗 Dataset**](https://huggingface.co/datasets/yale-nlp/DocMath-Eval) | [**📖 arXiv**](https://arxiv.org/abs/2311.09805) | [**GitHub**](https://github.com/yale-nlp/DocMath-Eval) |
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The data for the paper [DocMath-Eval: Evaluating Math Reasoning Capabilities of LLMs in Understanding Long and Specialized Documents](https://arxiv.org/abs/2311.09805). |
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**DocMath-Eval** is a comprehensive benchmark focused on numerical reasoning within specialized domains. It requires the model to comprehend long and specialized documents and perform numerical reasoning to answer the given question. |
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<p align="center"> |
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<img src="figures/overview.png" width="100%"> |
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</p> |
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## DocMath-Eval Dataset |
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All the data examples were divided into four subsets: |
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- **simpshort**, which is reannotated from [TAT-QA](https://aclanthology.org/2021.acl-long.254/) and [FinQA](https://aclanthology.org/2021.emnlp-main.300/), necessitates simple numerical reasoning over short document with one table |
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- **simplong**, which is reannotated from [MultiHiertt](https://aclanthology.org/2022.acl-long.454/), necessitates simple numerical reasoning over long document with multiple tables; |
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- **compshort**, which is reannotated from [TAT-HQA](https://aclanthology.org/2022.acl-long.5/), necessitates complex numerical reasoning over short document with one table; |
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- **complong**, which is annotated from scratch by our team, necessitates complex numerical reasoning over long document with multiple tables. |
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For each subset, we provide the *testmini* and *test* splits. |
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You can download this dataset by the following command: |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("yale-nlp/DocMath-Eval") |
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# print the first example on the complong testmini set |
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print(dataset["complong-testmini"][0]) |
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``` |
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The dataset is provided in json format and contains the following attributes: |
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``` |
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{ |
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"question_id": [string] The question id |
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"source": [string] The original source of the example (for simpshort, simplong, and compshort sets) |
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"original_question_id": [string] The original question id (for simpshort, simplong, and compshort sets) |
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"question": [string] The question text |
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"paragraphs": [list] List of paragraphs and tables within the document |
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"table_evidence": [list] List of indices in 'paragraphs' that are used as table evidence for the question |
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"paragraph_evidence": [list] List of indices in 'paragraphs' that are used as text evidence for the question |
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"python_solution": [string] Python-format and executable solution. This feature is hidden for the test set |
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"ground_truth": [float] Executed result of 'python_solution'. This feature is hidden for the test set |
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} |
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``` |
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## Contact |
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For any issues or questions, kindly email us at: Yilun Zhao ([email protected]). |
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## Citation |
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If you use the **DocMath-Eval** benchmark in your work, please kindly cite the paper: |
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``` |
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@misc{zhao2024docmatheval, |
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title={DocMath-Eval: Evaluating Math Reasoning Capabilities of LLMs in Understanding Long and Specialized Documents}, |
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author={Yilun Zhao and Yitao Long and Hongjun Liu and Ryo Kamoi and Linyong Nan and Lyuhao Chen and Yixin Liu and Xiangru Tang and Rui Zhang and Arman Cohan}, |
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year={2024}, |
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eprint={2311.09805}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2311.09805}, |
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} |
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``` |
