关键词引发的混乱：暴露大型语言模型对误导性关键词的谄媚性幻觉及防御策略评估
Chaos with Keywords: Exposing Large Language Models Sycophantic Hallucination to Misleading Keywords and Evaluating Defense Strategies
AI总结：
本研究揭示了大型语言模型面对误导性关键词时的谄媚性幻觉及其放大错误信息的风险，并评估了四种现有幻觉缓解策略在生成符合事实陈述上的有效性。
摘要：
本研究探讨了大型语言模型（LLMs）的谄媚倾向，即这些模型倾向于提供符合用户期望的答案，即使这些答案并非完全正确。这一探索的动机源于人们在互联网上搜索事实时带有部分或误导性知识的常见行为。类似于使用网络搜索引擎，用户可能会回忆起误导性关键词的片段并将其提交给LLM，期望获得全面的回复。我们对几个LLM的实证分析表明，当面对误导性关键词时，这些模型存在放大错误信息的潜在危险。此外，我们全面评估了四种现有的幻觉缓解策略，以减少LLMs的谄媚行为。我们的实验证明了这些策略在生成符合事实的正确陈述方面的有效性。进一步地，我们的分析深入探讨了对事实性关键词的知识探测实验以及谄媚缓解的不同类别。
英文摘要：
This study explores the sycophantic tendencies of Large Language Models (LLMs), where these models tend to provide answers that match what users want to hear, even if they are not entirely correct. The motivation behind this exploration stems from the common behavior observed in individuals searching the internet for facts with partial or misleading knowledge. Similar to using web search engines, users may recall fragments of misleading keywords and submit them to an LLM, hoping for a comprehensive response. Our empirical analysis of several LLMs shows the potential danger of these models amplifying misinformation when presented with misleading keywords. Additionally, we thoroughly assess four existing hallucination mitigation strategies to reduce LLMs sycophantic behavior. Our experiments demonstrate the effectiveness of these strategies for generating factually correct statements. Furthermore, our analyses delve into knowledge-probing experiments on factual keywords and different categories of sycophancy mitigation.
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。
