Permutation-Consensus Listwise Judging for Robust Factuality Evaluation
📰 ArXiv cs.AI
Learn to evaluate factuality in large language models using Permutation-Consensus Listwise Judging to reduce instability in decision-making
Action Steps
- Implement PCFJudge, an inference-time method, to rerun listwise factuality evaluation with different candidate orders
- Use permutation-consensus to aggregate results and reduce candidate-order sensitivity
- Evaluate the robustness of factuality evaluation using PCFJudge on a dataset with varying levels of hallucination risk
- Compare the performance of PCFJudge with existing factuality evaluation methods
- Apply PCFJudge to real-world applications, such as fact-checking and question-answering, to improve decision-making
Who Needs to Know This
NLP researchers and engineers working with large language models can benefit from this technique to improve the robustness of their factuality evaluation systems
Key Insight
💡 Permutation-Consensus Listwise Judging can reduce instability in factuality evaluation by aggregating results across different candidate orders
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Improve factuality evaluation in LLMs with Permutation-Consensus Listwise Judging #NLP #LLMs
Key Takeaways
Learn to evaluate factuality in large language models using Permutation-Consensus Listwise Judging to reduce instability in decision-making
Full Article
Title: Permutation-Consensus Listwise Judging for Robust Factuality Evaluation
Abstract:
arXiv:2603.20562v2 Announce Type: replace-cross 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 p
Abstract:
arXiv:2603.20562v2 Announce Type: replace-cross 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 p
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