citations能提升用户对AI回答的信任，但查证反而会降低信任
Citations and Trust in LLM Generated Responses
- 通过真实/随机引用对比实验，验证引用对信任的影响
- 有引用时信任显著提升，即使引用无关也有效
- 用户查证引用后信任下降，说明过度验证反噬信任
问答系统快速进步，但其黑箱特性可能影响用户信任。我们采用反监控框架，假设信任与引用存在正相关，且与查证行为负相关。通过实时问答实验，向参与者展示由商用聊天机器人生成的回答，附带0、1或5个引用（相关或随机），记录其是否查证及自评信任度。结果显示，存在引用时信任显著提升，该结果在引用随机时仍成立；而查证引用的行为导致信任显著下降。这些发现凸显了引用在增强用户对AI内容信任中的关键作用。
原文摘要 · Abstract (English)
Question answering systems are rapidly advancing, but their opaque nature may impact user trust. We explored trust through an anti-monitoring framework, where trust is predicted to be correlated with presence of citations and inversely related to checking citations. We tested this hypothesis with a live question-answering experiment that presented text responses generated using a commercial Chatbot along with varying citations (zero, one, or five), both relevant and random, and recorded if participants checked the citations and their self-reported trust in the generated responses. We found a significant increase in trust when citations were present, a result that held true even when the citations were random; we also found a significant decrease in trust when participants checked the citations. These results highlight the importance of citations in enhancing trust in AI-generated content.
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