Same Verdict, Different Readers: Personalizing Fact-Checking Explanations from Relative Preferences
Score
54Why selected
保留在全量动态池，可作为背景信息或后续聚合信号。
AI commentary
「来自 arXiv AI/CL/LG（论文） 的这条信息指向实践方法和工作流变化，适合用于判断近期 AI 圈的真实动向。」
Quick overview
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…
- 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.
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