Position: Evaluation Scores Are Perishable Knowledge Claims
Evaluation methodologies for language models increasingly combine multiple signals, from automated metrics and LLM-as-judge ratings to human assessments and benchmark suite results. When these signals are aggregated via averaging, evaluation confidence can then substantially exceed the reliability of the weakest signal: a phenomenon we call trust inflation in evaluation. We argue that evaluation scores should be treated as epistemic claims with three properties: formality (human evaluation provides stronger evidence than an automated metric), scope (a benchmark result applies to the tested distribution, not universally), and validity windows (benchmark results expire as contamination accu...
| Authors | Sankalp Gilda, Shlok Gilda |
|---|---|
| Primary category | cs.AI |
| Published | 28 July 2026 |
| Last updated at source | 28 July 2026 |
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