Quantifying and Auditing LLM Evaluation via Positive--Unlabeled Learning
Large Language Models (LLMs) are increasingly used as judges for scalable evaluation, yet such LLM--as--a--Judge systems exhibit systematic biases that are decoupled from semantic quality, most notably verbosity bias. Meanwhile, human supervision is costly and typically selective, yielding reliable positive judgments but leaving most outputs unlabelled and potentially mixed in quality. We formulate LLM evaluation under selective human supervision as a positive--unlabelled learning problem and propose a geometric auditing framework based on Partial Optimal Transport. By aligning a small set of human--verified positives with a reliable subset of unlabelled outputs in a fixed embedding space...
| Authors | Zilong Zhang, Yi-Ting Hung, Lei Ding et al. |
|---|---|
| Primary category | stat.ML |
| Published | 17 June 2026 |
| Last updated at source | 17 June 2026 |
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