Citations and Trust in LLM Generated Responses
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.
Authors
- Sanmitra Bhattacharya (ORCID: https://orcid.org/0000-0002-1697-3179)
- Tim Weninger (ORCID: https://orcid.org/0000-0003-3164-2615)
- Balaji Veeramani
- Sal Aguinaga
- Yifan Ding (ORCID: https://orcid.org/0000-0003-1973-8374)
- Matthew Facciani (ORCID: https://orcid.org/0000-0002-3695-4100)
- Amrit Poudel
- Ellen Joyce
Institutions
- University of Notre Dame (US)
- Deloitte (United States) (US)
Publication Details
- Journal
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2025-04-11
- DOI
- https://doi.org/10.1609/aaai.v39i22.34550
- Citations
- 11
- Primary Topic
- Natural Language Processing Techniques
- Type
- article
- Field-Weighted Citation Impact
- 9.75
Funders
- Defense Advanced Research Projects Agency