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.

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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

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article

Citations and Trust in LLM Generated Responses

Sanmitra Bhattacharya, Tim Weninger, Balaji Veeramani, Sal Aguinaga et al.
11 citations
Proceedings of the AAAI Conference on Artificial Intelligence
Natural Language Processing Techniques
9.75
article

Citations and Trust in LLM Generated Responses

Sanmitra Bhattacharya, Tim Weninger, Balaji Veeramani, Sal Aguinaga, Yifan Ding, Matthew Facciani, Amrit Poudel, Ellen Joyce
article en
11 citations

Abstract

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.

Proceedings of the AAAI Conference on Artificial IntelligenceVol. 39(22)
University of Notre Dame (US), Deloitte (United States) (US)
Defense Advanced Research Projects Agency
Openalex Percentile: Top 1%
Natural Language Processing Techniques
9.75
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