MARIO Eval: Evaluate Your Math LLM with your Math LLM--A mathematical dataset evaluation toolkit
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| Format: | Preprint |
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2024
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| _version_ | 1866909177439322112 |
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| author | Zhang, Boning Li, Chengxi Fan, Kai |
| author_facet | Zhang, Boning Li, Chengxi Fan, Kai |
| contents | Large language models (LLMs) have been explored in a variety of reasoning tasks including solving of mathematical problems. Each math dataset typically includes its own specially designed evaluation script, which, while suitable for its intended use, lacks generalizability across different datasets. Consequently, updates and adaptations to these evaluation tools tend to occur without being systematically reported, leading to inconsistencies and obstacles to fair comparison across studies. To bridge this gap, we introduce a comprehensive mathematical evaluation toolkit that not only utilizes a python computer algebra system (CAS) for its numerical accuracy, but also integrates an optional LLM, known for its considerable natural language processing capabilities. To validate the effectiveness of our toolkit, we manually annotated two distinct datasets. Our experiments demonstrate that the toolkit yields more robust evaluation results compared to prior works, even without an LLM. Furthermore, when an LLM is incorporated, there is a notable enhancement. The code for our method will be made available at \url{https://github.com/MARIO-Math-Reasoning/math_evaluation}. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_13925 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MARIO Eval: Evaluate Your Math LLM with your Math LLM--A mathematical dataset evaluation toolkit Zhang, Boning Li, Chengxi Fan, Kai Computation and Language Large language models (LLMs) have been explored in a variety of reasoning tasks including solving of mathematical problems. Each math dataset typically includes its own specially designed evaluation script, which, while suitable for its intended use, lacks generalizability across different datasets. Consequently, updates and adaptations to these evaluation tools tend to occur without being systematically reported, leading to inconsistencies and obstacles to fair comparison across studies. To bridge this gap, we introduce a comprehensive mathematical evaluation toolkit that not only utilizes a python computer algebra system (CAS) for its numerical accuracy, but also integrates an optional LLM, known for its considerable natural language processing capabilities. To validate the effectiveness of our toolkit, we manually annotated two distinct datasets. Our experiments demonstrate that the toolkit yields more robust evaluation results compared to prior works, even without an LLM. Furthermore, when an LLM is incorporated, there is a notable enhancement. The code for our method will be made available at \url{https://github.com/MARIO-Math-Reasoning/math_evaluation}. |
| title | MARIO Eval: Evaluate Your Math LLM with your Math LLM--A mathematical dataset evaluation toolkit |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2404.13925 |