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arxiv: 2501.01303 · v1 · pith:4I36WYLWnew · submitted 2025-01-02 · 💻 cs.CL · cs.AI

Citations and Trust in LLM Generated Responses

classification 💻 cs.CL cs.AI
keywords citationstrustgeneratedresponseswhencheckedfoundparticipants
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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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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Verified Misguidance: Measuring Structural Citation Failures in Search-Augmented LLMs

    cs.DL 2026-05 unverdicted novelty 7.0

    CITETRACE dataset and evaluation framework show 30.6% of citations distort sources and 27.1% use domain-inappropriate sources in search-augmented LLMs, with provider differences explaining 88-96% of quality variance.