CoChat
Sign Up
Cited by 27

The DOI matches its Crossref record.

Citations and Trust in LLM Generated Responses

Ding, Yifan, Facciani, Matthew, Joyce, Ellen, Poudel, Amrit, Bhattacharya, Sanmitra, Veeramani, Balaji, Aguinaga, Sal, Weninger, Tim·2025·Proceedings of the AAAI Conference on Artificial Intelligence
DOI 10.1609/aaai.v39i22.34550
Open this paper in CoChat — free account

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1609/aaai.v39i22.34550

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 40

  1. Achiam, J.; Adler, S.; Agarwal, S.; Ahmad, L.; Akkaya, I.; Aleman, F. L.; Almeida, D.; Altenschmidt, J.; Altman, S.; Anadkat, S.; et al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774. · arXiv
  2. Amaro, I.; Della Greca, A.; Francese, R.; Tortora, G.; and Tucci, C. 2023. AI unreliable answers: A case study on ChatGPT. In International Conference on Human-Computer Interaction, 23-40. Springer.
  3. Aoki, N. 2020. An experimental study of public trust in AI chatbots in the public sector. Government information quarterly, 37(4): 101490.
  4. Archard, D.; Deveaux, M.; Manson, N. A.; and Weinstock, D. M. 2013. Reading Onora O'Neill. Routledge Lon- don/New York, NY.
  5. Asai, A.; Wu, Z.; Wang, Y.; Sil, A.; and Hajishirzi, H. 2023. Self-rag: Learning to retrieve, generate, and critique through self-reflection. arXiv preprint arXiv:2310.11511. · arXiv
  6. Asai, A.; Zhong, Z.; Chen, D.; Koh, P. W.; Zettlemoyer, L.; Hajishirzi, H.; and Yih, W.-t. 2024. Reliable, adaptable, and attributable language models with retrieval. arXiv preprint arXiv:2403.03187. · arXiv
  7. Azaria, A.; and Mitchell, T. 2023. The internal state of an llm knows when its lying. arXiv preprint arXiv:2304.13734. · arXiv
  8. Baier, A. 1986. Trust and Antitrust. Ethics, 96(2): 231-260.
  9. Castelo, N.; and Ward, A. F. 2021. Conservatism predicts aversion to consequential Artificial Intelligence. Plos one, 16(12): e0261467.
  10. Cheng, H.-F.; Wang, R.; Zhang, Z.; O'connell, F.; Gray, T.; Harper, F. M.; and Zhu, H. 2019. Explaining decision- making algorithms through UI: Strategies to help non-expert stakeholders. In Proceedings of the 2019 chi conference on human factors in computing systems, 1-12.
  11. Choudhury, A.; and Shamszare, H. 2023. Investigating the impact of user trust on the adoption and use of ChatGPT: Survey analysis. Journal of Medical Internet Research, 25: e47184.
  12. Ditto, P. H.; Liu, B. S.; Clark, C. J.; Wojcik, S. P.; Chen, E. E.; Grady, R. H.; Celniker, J. B.; and Zinger, J. F. 2019. At least bias is bipartisan: A meta-analytic comparison of partisan bias in liberals and conservatives. Perspectives on Psychological Science, 14(2): 273-291.
  13. Ehsan, U.; Tambwekar, P.; Chan, L.; Harrison, B.; and Riedl, M. O. 2019. Automated rationale generation: a technique for explainable AI and its effects on human perceptions. In Pro- ceedings of the 24th international conference on intelligent user interfaces, 263-274.
  14. Ferrario, A.; and Loi, M. 2022. How Explainability Con- tributes to Trust in AI. In 2022 ACM Conference on Fair- ness, Accountability, and Transparency, 1457-1466. Seoul Republic of Korea: ACM. ISBN 978-1-4503-9352-2.
  15. Gonen, H.; Iyer, S.; Blevins, T.; Smith, N. A.; and Zettle- moyer, L. 2022. Demystifying prompts in language models via perplexity estimation. arXiv preprint arXiv:2212.04037. · arXiv
  16. Hoff, K. A.; and Bashir, M. 2015. Trust in automation: In- tegrating empirical evidence on factors that influence trust. Human factors, 57(3): 407-434.
  17. Kaplan, A. D.; Kessler, T. T.; Brill, J. C.; and Hancock, P. A. 2023. Trust in artificial intelligence: Meta-analytic findings. Human factors, 65(2): 337-359.
  18. Kim, Y. J.; Choi, J. H.; and Fotso, G. M. N. 2024. Med- ical professionals' adoption of AI-based medical devices: UTAUT model with trust mediation. Journal of Open Inno- vation: Technology, Market, and Complexity, 10(1): 100220.
  19. Lewis, P.; Perez, E.; Piktus, A.; Petroni, F.; Karpukhin, V.; Goyal, N.; Küttler, H.; Lewis, M.; Yih, W.-t.; Rock- täschel, T.; et al. 2020. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Infor- mation Processing Systems, 33: 9459-9474.
  20. Lins, S.; and Sunyaev, A. 2023. Advancing the presenta- tion of IS certifications: theory-driven guidelines for design- ing peripheral cues to increase users' trust perceptions. Be- haviour & Information Technology, 42(13): 2255-2278.
  21. Lipton, Z. C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue, 16(3): 31-57.
  22. Lucassen, T.; and Schraagen, J. M. 2011. Factual accuracy and trust in information: The role of expertise. Journal of the American Society for Information Science and Technology, 62(7): 1232-1242.
  23. McInnes, L.; Healy, J.; and Melville, J. 2018. Umap: Uni- form manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426. · arXiv
  24. Miller, D.; Johns, M.; Mok, B.; Gowda, N.; Sirkin, D.; Lee, K.; and Ju, W. 2016. Behavioral measurement of trust in au- tomation: the trust fall. In Proceedings of the human factors and ergonomics society annual meeting, volume 60, 1849- 1853. SAGE Publications Sage CA: Los Angeles, CA.
  25. Mothilal, R. K.; Sharma, A.; and Tan, C. 2020. Explain- ing machine learning classifiers through diverse counterfac- tual explanations. In Proceedings of the 2020 conference on fairness, accountability, and transparency, 607-617.
  26. Muir, B. M. 1987. Trust between humans and machines, and the design of decision aids. International journal of man-machine studies, 27(5-6): 527-539.
  27. Nothdurft, F.; Heinroth, T.; and Minker, W. 2013. The im- pact of explanation dialogues on human-computer trust. In Human-Computer Interaction. Users and Contexts of Use: 15th International Conference, HCI International 2013, Las Vegas, NV, USA, July 21-26, 2013, Proceedings, Part III 15, 59-67. Springer.
  28. Palan, S.; and Schitter, C. 2018. Prolific. ac-A subject pool for online experiments. Journal of Behavioral and Experi- mental Finance, 17: 22-27.
  29. Papenmeier, A.; Kern, D.; Englebienne, G.; and Seifert, C. 2022. It's complicated: The relationship between user trust, model accuracy and explanations in ai. ACM Transactions on Computer-Human Interaction (TOCHI), 29(4): 1-33.
  30. Poudel, A.; and Weninger, T. 2024. Navigating the Post-API Dilemma. In Proceedings of the ACM on Web Conference 2024, 2476-2484.
  31. Poursabzi-Sangdeh, F.; Goldstein, D. G.; Hofman, J. M.; Wortman Vaughan, J. W.; and Wallach, H. 2021. Manipu- lating and measuring model interpretability. In Proceedings of the 2021 CHI conference on human factors in computing systems, 1-52.
  32. Rawal, A.; McCoy, J.; Rawat, D. B.; Sadler, B. M.; and Amant, R. S. 2021. Recent advances in trustworthy ex- plainable artificial intelligence: Status, challenges, and per- spectives. IEEE Transactions on Artificial Intelligence, 3(6): 852-866.
  33. Reimers, N.; and Gurevych, I. 2019. Sentence-BERT: Sen- tence Embeddings using Siamese BERT-Networks. In Pro- ceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Association for Computa- tional Linguistics.
  34. Rosen, J. 1999. What are journalists for? Yale University Press.
  35. Stephanidis, C.; Salvendy, G.; Antona, M.; Chen, J. Y.; Dong, J.; Duffy, V. G.; Fang, X.; Fidopiastis, C.; Fragomeni, G.; Fu, L. P.; et al. 2019. Seven HCI grand challenges. Inter- national Journal of Human-Computer Interaction, 35(14): 1229-1269.
  36. Sun, Z.; Wang, X.; Tay, Y.; Yang, Y.; and Zhou, D. 2022. Recitation-augmented language models. arXiv preprint arXiv:2210.01296. · arXiv
  37. Touvron, H.; Martin, L.; Stone, K.; Albert, P.; Almahairi, A.; Babaei, Y.; Bashlykov, N.; Batra, S.; Bhargava, P.; Bhosale, S.; et al. 2023. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288. · arXiv
  38. Venkatesh, V.; and Davis, F. D. 2000. A theoretical exten- sion of the technology acceptance model: Four longitudinal field studies. Management science, 46(2): 186-204.
  39. Zhang, Y.; Liao, Q. V.; and Bellamy, R. K. 2020. Effect of confidence and explanation on accuracy and trust cali- bration in AI-assisted decision making. In Proceedings of the 2020 conference on fairness, accountability, and trans- parency, 295-305.
  40. Zhao, H.; Chen, H.; Yang, F.; Liu, N.; Deng, H.; Cai, H.; Wang, S.; Yin, D.; and Du, M. 2024. Explainability for large language models: A survey. ACM Transactions on Intelli- gent Systems and Technology, 15(2): 1-38.

Cited by

Sorted by citations

Metadata

Authors
Ding, Y., Facciani, M., Joyce, E., Poudel, A., Bhattacharya, S., Veeramani, B., Aguinaga, S., & Weninger, T.
Venue
Proceedings of the AAAI Conference on Artificial Intelligence
Publisher
Association for the Advancement of Artificial Intelligence (AAAI)
Published
2025-04-11
Volume
39
Issue
22
Pages
23787-23795
ISSN
  • 2374-3468
  • 2159-5399
DOI
10.1609/aaai.v39i22.34550
Cited by
27
References
0

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review