不同程度的虚假内容：警告如何影响人类对大语言模型幻觉的感知与参与度
Fakes of Varying Shades: How Warning Affects Human Perception and Engagement Regarding LLM Hallucinations
AI总结：
本研究通过419名参与者的实验，探究不同幻觉程度的LLM内容与警告提示对人类感知和参与行为的影响，发现警告可提升幻觉检测能力且不干扰真实内容判断，为相关检测工具开发提供参考。
摘要：
大语言模型（LLM）的广泛应用与变革性影响引发了人们对其生成不准确、虚构内容（即“幻觉”）能力的担忧。鉴于幻觉带来的潜在风险，人类应当能够识别这类内容。本研究旨在通过系统改变幻觉程度（真实内容、轻微幻觉、严重幻觉），并考察其与警告（即潜在不准确内容的提示：无警告/有警告）的交互作用，探究人类对LLM幻觉的感知。研究招募了来自Prolific平台的419名参与者，让他们以问答形式对内容的感知准确性进行评分，并与内容产生互动（如点赞、点踩、分享）。结果显示，参与者对内容真实度的排序为真实内容、轻微幻觉、严重幻觉，用户参与行为也符合这一模式。更重要的是，研究发现警告提升了幻觉检测能力，且未显著影响真实内容的感知真实度。最后，本研究为未来辅助人类检测幻觉的工具提供了洞见。所有调查材料、人口统计学问题及会后问题可在以下网址获取：https://github.com/MahjabinNahar/fakes-of-varying-shades-survey-materials
英文摘要：
The widespread adoption and transformative effects of large language models (LLMs) have sparked concerns regarding their capacity to produce inaccurate and fictitious content, referred to as `hallucinations'. Given the potential risks associated with hallucinations, humans should be able to identify them. This research aims to understand the human perception of LLM hallucinations by systematically varying the degree of hallucination (genuine, minor hallucination, major hallucination) and examining its interaction with warning (i.e., a warning of potential inaccuracies: absent vs. present). Participants (N=419) from Prolific rated the perceived accuracy and engaged with content (e.g., like, dislike, share) in a Q/A format. Participants ranked content as truthful in the order of genuine, minor hallucination, and major hallucination, and user engagement behaviors mirrored this pattern. More importantly, we observed that warning improved the detection of hallucination without significantly affecting the perceived truthfulness of genuine content. We conclude by offering insights for future tools to aid human detection of hallucinations. All survey materials, demographic questions, and post-session questions are available at: https://github.com/MahjabinNahar/fakes-of-varying-shades-survey-materials
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。
