Hard perturbations that change the required solution method cause 10-25% accuracy drops across 18 LLMs on MATH, revealing limits in reasoning robustness.
What does it mean for a language model to preserve privacy? InProceedings of the 2022 ACM conference on fairness, accountability, and transparency, pages 2280–2292,
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MATH-Perturb: Benchmarking LLMs' Math Reasoning Abilities against Hard Perturbations
Hard perturbations that change the required solution method cause 10-25% accuracy drops across 18 LLMs on MATH, revealing limits in reasoning robustness.