AI RESEARCH
Towards Reliable LLM Evaluation: Correcting the Winner's Curse in Adaptive Benchmarking
arXiv CS.LG
•
ArXi:2605.05973v1 Announce Type: cross Adaptive prompt and program search makes LLM evaluation selection-sensitive. Once benchmark items are reused inside tuning, the observed winner's score need not estimate the fresh-data performance of the full tune-then-deploy procedure. We study inference for this procedure-level target under explicit tuning budgets.