引用在偏好数据中的作用研究

AI 摘要

该论文研究了引用在人类和LLM偏好判断中的作用，基于SciArena和ResearchQA两个科学问答数据集，使用混合效应模型分析。主要发现包括：人类偏好引用更多样但总数更少的回答；LLM对引用的偏好与人类不同，且不同LLM之间也存在差异。研究结果对偏好数据收集和奖励建模具有启示意义。

正文节选

On the Role of Citations in Preference Data Abstract Many NLP tasks require systems to provide attribution in their outputs—i.e. citations to grounding sources. Attribution serves as a bulwark against model hallucination and as a means for users to verify the credibility of model outputs. Yet, it is unclear how humans and LLMs evaluate citations when comparing outputs, a process central to reward modeling and modern LLM post-training. This paper studies the role of citations in the preferences o

发布时间：2026-08-25 12:00

抓取时间：2026-08-25 12:08

来源机构：arXiv