标题： 引用和大语言模型生成回应中的信任 显示英文标题

标题： Citations and Trust in LLM Generated Responses

摘要： 问答系统正在迅速发展，但其不透明的性质可能会影响用户的信任。 我们通过一个反监控框架来探索信任，其中信任被预测与引用的存在相关，并且与检查引用成反比。 我们通过一个实时问答实验测试了这一假设，该实验展示了使用商业聊天机器人生成的文本回答，并附有不同数量的引用（零个、一个或五个），包括相关引用和随机引用，并记录参与者是否检查了引用以及他们对生成回答的自我报告信任度。 我们发现当存在引用时信任显著增加，即使引用是随机的，这一结果仍然成立；我们还发现当参与者检查引用时，信任显著下降。 这些结果突显了引用在增强对人工智能生成内容的信任中的重要性。 显示英文摘要

摘要： 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.