arXiv cs.CL· Haokun Wen, Xuemeng Song, Mong-Li Lee, Wynne Hsu·· 13 小时前AI 评分27
PERPL：基于相对偏好为不同读者个性化改写事实核查解释
Same Verdict, Different Readers: Personalizing Fact-Checking Explanations from Relative Preferences
AI 导读
研究者提出 PERPL 框架，通过 LLM 模拟读者的相对偏好学习个性化改写，在保留事实忠实度的同时让事实核查解释适配不同读者。该框架先用跨人设互配对构建偏好监督，再以探索引导的偏好优化训练轻量改写器，使其能发现预定义风格之外的偏好解释。实验显示 PERPL 在个性化偏好对齐上优于规模更大的通用 LLM，并保持高事实忠实度。
正文 · 原文
Abstract:Fact-checking explanations help Web users understand why a claim receives its verdict, yet the same explanation may not satisfy readers with different backgrounds and preferences. We formulate Faithful Personalized Fact-Checking Explanation Rewriting, which adapts an existing explanation to individual readers while preserving factual fidelity. Rather than relying on reader-written reference explanations, we learn personalized rewriting from the relative preferences of LLM-simulated readers. However, this presents two challenges: distinguishing persona-specific preferences from general explanation quality and learning beyond a limited set of predefined explanation styles. To address these challenges, we propose Personalized Explanation Rewriting via Preference Learning (PERPL), a framework consisting of two components. Simulated Preference Construction derives persona-specific supervision from simulated reader preferences through cross-persona reciprocal pairing. Progressive Preference Learning first trains a lightweight rewriter on the constructed supervision and then refines it through exploration-guided preference optimization, enabling it to discover preferred explanations beyond the predefined styles. Extensive experiments demonstrate that PERPL outperforms much larger general-purpose LLMs in personalized preference alignment while maintaining high factual fidelity.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.37380 [cs.CL] |
| (or arXiv:2610.37380v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.37380 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Haokun Wen [view email]
[v1]
Tue, 27 Oct 2026 15:08:30 UTC (1,064 KB)
来源：arXiv cs.CL · arxiv.org