Metric Match: A Subset Selection Approach to Evaluating LLM Judge Reliability
Researchers have developed Metric Match, a novel method to estimate the reliability of LLM judges using limited human annotations. By selecting an optimal subset of samples, it reduces annotation needs by 32.5% and significantly cuts down evaluation costs.
Computer Science > Artificial Intelligence
Title:Metric Match: A Subset Selection Approach to Evaluating LLM Judge Reliability
View PDF HTML (experimental)Abstract:LLM judges are used to reduce the need for costly human labor in evaluating open-ended text generation. However, the reliability of these judges depends critically on their alignment with human raters -- a property that itself depends on costly human annotations. In this work, we develop a method (Metric Match) for estimating correlation-based reliability metrics of LLM judges from limited annotations. Metric Match selects a subset of samples for human annotation such that the subset matches the population reliability metric with respect to acquired synthetic labels. We empirically show that Metric Match achieves a win-rate of 0.838 against random subset selection across four different correlation metrics and 15 datasets, with an 18.7% decrease in average estimation error and reduces annotation needs by 32.5%. We provide a cost model and highlight a medical case study where our method saves $1,041.67 compared to random selection for expert annotation. Further, we shift our task from reliability estimation to reliability classification of whether a given judge is above a deployment threshold, outperforming random selection with Metric Match. All project code is publicly available, and we additionally provide an installable package for ease of use.
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Source: arXiv cs.AI Recent
