| Challenge: | Prevailing benchmarks for mathematical reasoning include MATH and AIME . predicated on single-instantiation problems with fixed numbers, these models leave generalization on isomorphic problem variants untested. |
| Approach: | They propose a mathematical reasoning benchmark that quantifies solution accuracy and solution space generality. |
| Outcome: | The proposed model solves 1,053 problems spanning 9 mathematical domains . the best-performing model solved only 32.57% of the problems . |
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| Challenge: | Existing evaluation frameworks for large reasoning models are saturated by a lack of reliable and verifiable benchmarks. |
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Yan Liu, Minghui Zhang, Bojian Xiong, Yifan Xiao, Yinong Sun, Yating Mei, Longyu Zeng, Jingchao Yang, Yang Wang, Deyi Xiong
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| Challenge: | Large language models (LLMs) have demonstrated impressive performance across various mathematical reasoning benchmarks. |
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NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks (2022.acl-long)
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Swaroop Mishra, Arindam Mitra, Neeraj Varshney, Bhavdeep Sachdeva, Peter Clark, Chitta Baral, Ashwin Kalyan
| Challenge: | Existing AI systems fail to perform basic mathematical reasoning when presented in a slightly different manner. |
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