研究大模型幻觉如何影响用户信任，发现信任会随情境动态调整。
Calibrated Trust in Dealing with LLM Hallucinations: A Qualitative Study
- 通过192人访谈，发现用户对幻觉并非全盘不信，而是根据情境调整信任。
- 确认期望、经验、专业知识等为关键信任因素，新增直觉作为检测幻觉的线索。
- 适合关注AI可信性、人机交互设计的研究者与产品开发者参考。
大语言模型（LLMs）的幻觉指输出内容在事实层面错误但表象合理[1]。本文通过包含192名参与者的定性研究，探讨幻觉如何影响用户对LLM的信任及交互行为。研究发现，幻觉并未导致普遍性不信任，而是引发情境化信任校准。基于Lee & See [2]的校准信任模型及Afroogh等[3]的信任因素，本文验证了期望[3][4]、先前经验[3][4][5]、用户专业能力与领域知识[3][4]等人因信任因素，并识别出直觉作为辅助幻觉检测的新增因素。此外，信任动态还受情境因素影响，尤其是感知风险[3]与决策重要性[6]。据此，本文验证并扩展了Blöbaum提出的递归信任校准过程，将直觉纳入人因信任因素。基于上述发现，提出负责任且具反思性的LLM使用建议。
原文摘要 · Abstract (English)
Hallucinations are outputs by Large Language Models (LLMs) that are factually incorrect yet appear plausible [1]. This paper investigates how such hallucinations influence users' trust in LLMs and users' interaction with LLMs. To explore this in everyday use, we conducted a qualitative study with 192 participants. Our findings show that hallucinations do not result in blanket mistrust but instead lead to context-sensitive trust calibration. Building on the calibrated trust model by Lee & See [2] and Afroogh et al.'s trust-related factors [3], we confirm expectancy [3], [4], prior experience [3], [4], [5], and user expertise & domain knowledge [3], [4] as userrelated (human) trust factors, and identify intuition as an additional factor relevant for hallucination detection. Additionally, we found that trust dynamics are further influenced by contextual factors, particularly perceived risk [3] and decision stakes [6]. Consequently, we validate the recursive trust calibration process proposed by Blöbaum [7] and extend it by including intuition as a user-related trust factor. Based on these insights, we propose practical recommendations for responsible and reflective LLM use.
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