Developer Finds His LLM Eval Harness Was Grading Format, Not Factual Accuracy

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A developer building an LLM drift-detection suite discovered that his exact-match grader was penalizing correct answers due to formatting differences, not factual errors. Tasks asking for the chemical symbol for gold and the smallest planet recorded near-100% failure rates across GPT-4o mini, Claude Sonnet 5, and Llama 3.1 8B, even though all models knew the correct answers. The models were responding with full sentences or markdown styling such as '**Au**' rather than the bare two-letter string the grader expected. A near-identical task asking for a country capital passed cleanly because its instructions specified an exact output shape, exposing the inconsistency in how constraints were worded across the suite. The developer notes the measurement is reliable but the precise explanation is still uncertain, and warns others maintaining exact-match eval suites to audit which tasks have failed consistently without anyone checking the raw model output.

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