GSM-SEM: Benchmark and Framework for Generating Semantically Variant Augmentations
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| Autores principales: | , , , , , , , , , , , , , |
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| Formato: | Preprint |
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2026
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| author | Singh, Jyotika Tu, Fang Mirsaidova, Aziza Agarwal, Amit Patel, Hitesh Laxmichand Ghoshal, Sandip Ballesteros, Miguel Dua, Karan Benajiba, Yassine Sun, Weiyi Sheng, Tao Horwood, Graham Ravi, Sujith Roth, Dan |
| author_facet | Singh, Jyotika Tu, Fang Mirsaidova, Aziza Agarwal, Amit Patel, Hitesh Laxmichand Ghoshal, Sandip Ballesteros, Miguel Dua, Karan Benajiba, Yassine Sun, Weiyi Sheng, Tao Horwood, Graham Ravi, Sujith Roth, Dan |
| contents | Benchmarks like GSM8K are popular measures of mathematical reasoning, but leaderboard gains can overstate true capability due to memorization of fixed test sets. Most robustness variants apply surface-level perturbations (paraphrases, renamings, number swaps, distractors) that largely preserve the underlying facts, and static releases can themselves become memorization targets over time. We introduce GSM-SEM, a reusable and stochastic framework for generating semantically diverse benchmark variants with substantially higher semantic variance than prior approaches. GSM-SEM perturbs problem statements by modifying entities, attributes, and/or relationships, frequently altering underlying facts and requiring models to recompute solutions under new conditions, while constraining generation to preserve the original calculations/answer and approximate problem difficulty. GSM-SEM generates fresh variants on each run without requiring re-annotation, reducing reliance on static public benchmarks for evaluation and thereby lowering the bias of memorization. We apply GSM-SEM on GSM8K and two existing variation suites (GSM-Symbolic and GSM-Plus), producing GSM8K-SEM, GSM-Symbolic-SEM, and GSM-Plus-SEM. Evaluating 14 SOTA LLMs, we observe consistent performance drops with larger decline when semantic perturbations are coupled with symbolic/plus variations (average drop rate 28% in maximum strictness configuration of GSM-SEM). We publicly release the three SEM variants as fully human-validated datasets. Finally, to demonstrate applicability beyond GSM-style math problems, we apply GSM-SEM to additional benchmarks including BigBenchHard, LogicBench, and NLR-BIRD. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_07053 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GSM-SEM: Benchmark and Framework for Generating Semantically Variant Augmentations Singh, Jyotika Tu, Fang Mirsaidova, Aziza Agarwal, Amit Patel, Hitesh Laxmichand Ghoshal, Sandip Ballesteros, Miguel Dua, Karan Benajiba, Yassine Sun, Weiyi Sheng, Tao Horwood, Graham Ravi, Sujith Roth, Dan Computation and Language Artificial Intelligence Benchmarks like GSM8K are popular measures of mathematical reasoning, but leaderboard gains can overstate true capability due to memorization of fixed test sets. Most robustness variants apply surface-level perturbations (paraphrases, renamings, number swaps, distractors) that largely preserve the underlying facts, and static releases can themselves become memorization targets over time. We introduce GSM-SEM, a reusable and stochastic framework for generating semantically diverse benchmark variants with substantially higher semantic variance than prior approaches. GSM-SEM perturbs problem statements by modifying entities, attributes, and/or relationships, frequently altering underlying facts and requiring models to recompute solutions under new conditions, while constraining generation to preserve the original calculations/answer and approximate problem difficulty. GSM-SEM generates fresh variants on each run without requiring re-annotation, reducing reliance on static public benchmarks for evaluation and thereby lowering the bias of memorization. We apply GSM-SEM on GSM8K and two existing variation suites (GSM-Symbolic and GSM-Plus), producing GSM8K-SEM, GSM-Symbolic-SEM, and GSM-Plus-SEM. Evaluating 14 SOTA LLMs, we observe consistent performance drops with larger decline when semantic perturbations are coupled with symbolic/plus variations (average drop rate 28% in maximum strictness configuration of GSM-SEM). We publicly release the three SEM variants as fully human-validated datasets. Finally, to demonstrate applicability beyond GSM-style math problems, we apply GSM-SEM to additional benchmarks including BigBenchHard, LogicBench, and NLR-BIRD. |
| title | GSM-SEM: Benchmark and Framework for Generating Semantically Variant Augmentations |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2605.07053 |