arXiv:2601.21471v2 Announce Type: replace

Abstract: In service system design, it is often the case that the main evidence of system performance appears in textual form, such as customer support transcripts, complaint narratives, and compliance review reports. With the increasing use of large language models (LLMs), these textual trajectories can be converted into low-cost proxy scores that support comparison and optimization of candidate service configurations. However, LLM-based evaluations can exhibit systematic bias and require human audits for correction. To this end, this research studies how to identify the best service configuration with high confidence while minimizing the use of expensive human audits. We formulate the problem as fixed-confidence best arm identification with an always-observed, potentially biased proxy and selectively acquired human-verified outcomes. Moreover, several configurations can run on one case. A human audit can provide a case label that scores all their outputs. We show that sharing this reference and deciding whether to audit after observing proxy scores can each strictly reduce asymptotic identification cost. The selected configurations determine the available proxy scores and verified outcomes, so the proposed algorithm allocates configuration evaluations and subsequent audits jointly. For a specified finite family of data-generating models containing the true law and distinguishing different best configurations, its expected cost asymptotically attains the information-theoretic lower bound. For audits of individual outputs, we develop prediction-powered confidence sequences valid under adaptive auditing and delayed feedback. Numerical experiments on synthetic examples and service datasets show that the proposed algorithms require substantially less human review while maintaining reliable selection.