U-MATH: A University-Level Benchmark for Evaluating Mathematical Skills in LLMs
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arXiv
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| Format: | Preprint |
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2024
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| author | Chernyshev, Konstantin Polshkov, Vitaliy Artemova, Ekaterina Myasnikov, Alex Stepanov, Vlad Miasnikov, Alexei Tilga, Sergei |
| author_facet | Chernyshev, Konstantin Polshkov, Vitaliy Artemova, Ekaterina Myasnikov, Alex Stepanov, Vlad Miasnikov, Alexei Tilga, Sergei |
| contents | The current evaluation of mathematical skills in LLMs is limited, as existing benchmarks are either relatively small, primarily focus on elementary and high-school problems, or lack diversity in topics. Additionally, the inclusion of visual elements in tasks remains largely under-explored.
To address these gaps, we introduce U-MATH, a novel benchmark of 1,100 unpublished open-ended university-level problems sourced from teaching materials. It is balanced across six core subjects, with 20% of multimodal problems. Given the open-ended nature of U-MATH problems, we employ an LLM to judge the correctness of generated solutions. To this end, we release $μ$-MATH, a dataset to evaluate the LLMs' capabilities in judging solutions.
Benchmarking leading LLMs reveals marked limitations in multi-modal reasoning, with maximum accuracy reaching 93.1\% on textual tasks but only 58.5\% on visual ones. Furthermore, solution judgment proves challenging, requiring the most advanced models to achieve meaningfully high performance, even still peaking at an imperfect F1-score of 90.1\%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_03205 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | U-MATH: A University-Level Benchmark for Evaluating Mathematical Skills in LLMs Chernyshev, Konstantin Polshkov, Vitaliy Artemova, Ekaterina Myasnikov, Alex Stepanov, Vlad Miasnikov, Alexei Tilga, Sergei Computation and Language Artificial Intelligence The current evaluation of mathematical skills in LLMs is limited, as existing benchmarks are either relatively small, primarily focus on elementary and high-school problems, or lack diversity in topics. Additionally, the inclusion of visual elements in tasks remains largely under-explored. To address these gaps, we introduce U-MATH, a novel benchmark of 1,100 unpublished open-ended university-level problems sourced from teaching materials. It is balanced across six core subjects, with 20% of multimodal problems. Given the open-ended nature of U-MATH problems, we employ an LLM to judge the correctness of generated solutions. To this end, we release $μ$-MATH, a dataset to evaluate the LLMs' capabilities in judging solutions. Benchmarking leading LLMs reveals marked limitations in multi-modal reasoning, with maximum accuracy reaching 93.1\% on textual tasks but only 58.5\% on visual ones. Furthermore, solution judgment proves challenging, requiring the most advanced models to achieve meaningfully high performance, even still peaking at an imperfect F1-score of 90.1\%. |
| title | U-MATH: A University-Level Benchmark for Evaluating Mathematical Skills in LLMs |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2412.03205 |