Citations and Trust in LLM Generated Responses
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866909447134117888 |
|---|---|
| author | Ding, Yifan Facciani, Matthew Poudel, Amrit Joyce, Ellen Aguinaga, Salvador Veeramani, Balaji Bhattacharya, Sanmitra Weninger, Tim |
| author_facet | Ding, Yifan Facciani, Matthew Poudel, Amrit Joyce, Ellen Aguinaga, Salvador Veeramani, Balaji Bhattacharya, Sanmitra Weninger, Tim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_01303 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Citations and Trust in LLM Generated Responses Ding, Yifan Facciani, Matthew Poudel, Amrit Joyce, Ellen Aguinaga, Salvador Veeramani, Balaji Bhattacharya, Sanmitra Weninger, Tim Computation and Language Artificial Intelligence 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. |
| title | Citations and Trust in LLM Generated Responses |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2501.01303 |