GSM-SEM: Benchmark and Framework for Generating Semantically Variant Augmentations
📰 ArXiv cs.AI
Learn how to generate semantically variant augmentations with GSM-SEM, a framework for robust benchmarking and improved mathematical reasoning capabilities
Action Steps
- Apply GSM-SEM to generate semantically variant augmentations for mathematical reasoning tasks
- Use the framework to create stochastic and reusable benchmarks
- Evaluate the robustness of AI models using GSM-SEM
- Compare the performance of different models on GSM-SEM-generated augmentations
- Integrate GSM-SEM into existing benchmarking pipelines to improve overall model reliability
Who Needs to Know This
Researchers and developers working on mathematical reasoning and AI benchmarking can benefit from this framework to improve the robustness of their models and avoid overfitting to fixed test sets
Key Insight
💡 GSM-SEM provides a reusable and stochastic framework for generating semantically variant augmentations, helping to avoid overfitting and improve model robustness
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📈 Introducing GSM-SEM: a framework for generating semantically variant augmentations to improve mathematical reasoning capabilities 🤖
Key Takeaways
Learn how to generate semantically variant augmentations with GSM-SEM, a framework for robust benchmarking and improved mathematical reasoning capabilities
Full Article
Title: GSM-SEM: Benchmark and Framework for Generating Semantically Variant Augmentations
Abstract:
arXiv:2605.07053v1 Announce Type: cross Abstract: 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 f
Abstract:
arXiv:2605.07053v1 Announce Type: cross Abstract: 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 f
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