MathGAP: Out-of-Distribution Evaluation on Problems with Arbitrarily Complex Proofs
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arXiv
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| Format: | Preprint |
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2024
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| author | Opedal, Andreas Shirakami, Haruki Schölkopf, Bernhard Saparov, Abulhair Sachan, Mrinmaya |
| author_facet | Opedal, Andreas Shirakami, Haruki Schölkopf, Bernhard Saparov, Abulhair Sachan, Mrinmaya |
| contents | Large language models (LLMs) can solve arithmetic word problems with high accuracy, but little is known about how well they generalize to more complex problems. This is difficult to study, as (i) much of the available evaluation data has already been seen by the most capable models during training, and (ii) existing benchmarks do not capture how problem proofs may be arbitrarily complex in various ways. In this paper, we present a data-generation framework for evaluating LLMs on problems with arbitrarily complex arithmetic proofs, called MathGAP. MathGAP generates problem statements and chain-of-thought reasoning traces according to specifications about their arithmetic proof structure, enabling systematic studies on easy-to-hard generalization with respect to complexity of proof trees. Using MathGAP, we find that LLMs show a significant decrease in performance as proofs get deeper and wider. This effect is more pronounced in complex, nonlinear proof structures, which are challenging even for the most capable models. The models are also sensitive to simple changes in sentence ordering. However, they remain capable of solving some complex problems, suggesting that reasoning generalization is noisy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_13502 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MathGAP: Out-of-Distribution Evaluation on Problems with Arbitrarily Complex Proofs Opedal, Andreas Shirakami, Haruki Schölkopf, Bernhard Saparov, Abulhair Sachan, Mrinmaya Machine Learning Artificial Intelligence Computation and Language Large language models (LLMs) can solve arithmetic word problems with high accuracy, but little is known about how well they generalize to more complex problems. This is difficult to study, as (i) much of the available evaluation data has already been seen by the most capable models during training, and (ii) existing benchmarks do not capture how problem proofs may be arbitrarily complex in various ways. In this paper, we present a data-generation framework for evaluating LLMs on problems with arbitrarily complex arithmetic proofs, called MathGAP. MathGAP generates problem statements and chain-of-thought reasoning traces according to specifications about their arithmetic proof structure, enabling systematic studies on easy-to-hard generalization with respect to complexity of proof trees. Using MathGAP, we find that LLMs show a significant decrease in performance as proofs get deeper and wider. This effect is more pronounced in complex, nonlinear proof structures, which are challenging even for the most capable models. The models are also sensitive to simple changes in sentence ordering. However, they remain capable of solving some complex problems, suggesting that reasoning generalization is noisy. |
| title | MathGAP: Out-of-Distribution Evaluation on Problems with Arbitrarily Complex Proofs |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2410.13502 |