How Sensitive Are LLM Leaderboard Claims to Hidden Model Selection?
LLM leaderboard gains can reflect selection among privately evaluated model variants, yet neither the number of variants nor their dependence is public. We ask how many hidden variants a published margin can support while retaining statistical evidence of a provider's advantage over a fixed comparator. For a fixed candidate family under a Gaussian margin model, we derive a sensitivity curve that reports this maximum count as a function of a lower bound on within-family correlation. The relevant correlation must match the score used for ranking and the sampling model: in a controlled family, pooled item correlation is 0.90, whereas composite-score correlation is 0.46 under item resampling ...
| Authors | Chen Yang, Jun Chen |
|---|---|
| Primary category | stat.ML |
| Published | 23 September 2026 |
| Last updated at source | 25 September 2026 |
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