Permutation-Consensus Listwise Judging for Robust Factuality Evaluation

• URL: http://arxiv.org/abs/2603.20562v1
• Date: Fri, 20 Mar 2026 23:35:14 GMT
• Title: Permutation-Consensus Listwise Judging for Robust Factuality Evaluation
• Abstract summary: We study candidate-order sensitivity in listwise factuality evaluation.<n>We introduce PCFJudge, an inference-time method that reruns the same factuality-first listwise prompt over multiple orderings of the same candidate set.<n>On RewardBench 2 Factuality, PCFJudge improves over direct judging by up to 7 absolute points.
• Score: 0.8563354084119061
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large language models (LLMs) are now widely used as judges, yet their decisions can change under presentation choices that should be irrelevant. We study one such source of instability: candidate-order sensitivity in listwise factuality evaluation, where several answers can look similarly polished while differing sharply in hallucination risk. We introduce PCFJudge, an inference-time method that reruns the same factuality-first listwise prompt over multiple orderings of the same candidate set and aggregates the resulting scores, ranks, and uncertainty signals into a single consensus decision. On RewardBench 2 Factuality, PCFJudge improves over direct judging by up to 7 absolute points. Development ablations show that the dominant gain comes from permutation consensus itself rather than from heavier arbitration layers. These results suggest that a meaningful share of factuality-judging error arises from order instability, and that averaging over this nuisance variation is a simple and effective way to make LLM evaluation more reliable.

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