Citations and Trust in LLM Generated Responses
This addresses trust issues in AI question-answering systems for users, though it is incremental as it builds on existing anti-monitoring frameworks.
The study investigated how citations affect user trust in LLM-generated responses, finding that trust significantly increased when citations were present (even random ones) and decreased when users checked the citations.
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.