LLM在（不）知道时能否进行语境锚定：一项关于直接政治问题与诱导性政治问题的研究
Can LLMs Ground when they (Don't) Know: A Study on Direct and Loaded Political Questions
AI总结：
本文通过直接政治知识问题与预设错误信息的诱导性问题，评估LLM能否主动锚定共同语境并纠正错误信念，发现其相关能力存在显著缺陷。
摘要：
人类之间的交流依赖于会话锚定，使对话者即使在不具备完备知识、且必须解决彼此信念差异的情况下，也能达成相互理解。本文研究大型语言模型（LLM）在自身（不）拥有知识的情形下如何管理共同语境，重点关注政治领域中的事实，因为该领域错误信息和锚定失败的风险很高。我们考察LLM回答直接知识问题以及预设了错误信息的诱导性问题的能力。我们评估诱导性问题是否会促使LLM进行主动锚定并纠正用户的错误信念，并将其与LLM的知识水平和政治偏见联系起来。研究结果突显了LLM在进行语境锚定和拒绝用户错误信念方面存在的重大挑战，也引发了人们对其在缓解政治话语中错误信息方面所扮演角色的担忧。
英文摘要：
Communication among humans relies on conversational grounding, allowing interlocutors to reach mutual understanding even when they do not have perfect knowledge and must resolve discrepancies in each other's beliefs. This paper investigates how large language models (LLMs) manage common ground in cases where they (don't) possess knowledge, focusing on facts in the political domain where the risk of misinformation and grounding failure is high. We examine the ability of LLMs to answer direct knowledge questions and loaded questions that presuppose misinformation. We evaluate whether loaded questions lead LLMs to engage in active grounding and correct false user beliefs, in connection to their level of knowledge and their political bias. Our findings highlight significant challenges in LLMs' ability to engage in grounding and reject false user beliefs, raising concerns about their role in mitigating misinformation in political discourse.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
