Researcher: benchmark biases hide LLM flaws, like extreme-value numeric distractors
antoine_chaffin · x · 2026-10-29
Researcher antoinechaffin argues that models relying on matching benchmark settings (how options are arranged, distractor placement) rather than truly solving tasks will fail in real use — and such flaws don't show on held-out benchmarks because those share the same biases. Concrete example: asking an LLM to generate distractors for numerical questions commonly puts answers as extreme values in the options, a bias that transfers heavily to some benchmarks. The flaw only surfaces when real users rely on the model.
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