Resurrecting saturated LLM benchmarks with adversarial encoding

Igor Ivanov; Dmitrii Volkov

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arXiv · 2502.06738

Resurrecting saturated LLM benchmarks with adversarial encoding

Igor Ivanov·Dmitrii Volkov

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Abstract

Recent work showed that small changes in benchmark questions can reduce LLMs' reasoning and recall. We explore two such changes: pairing questions and adding more answer options, on three benchmarks: WMDP-bio, GPQA, and MMLU variants. We find that for more capable models, these predictably reduce performance, essentially heightening the performance ceiling of a benchmark and unsaturating it again. We suggest this approach can resurrect old benchmarks.

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Igor Ivanov, Dmitrii Volkov. 2025-02-10. Resurrecting saturated LLM benchmarks with adversarial encoding. https://arxiv.org/abs/2502.06738

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