Pith. sign in

REVIEW 3 major objections 5 minor 53 references

Citations and Trust in LLM Generated Responses

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Citations raise trust in AI chatbot answers even when they are random, while checking them signals distrust.

desk verdict Solid experiment, overstated abstract: the random-citation headline isn't directly tested, but the citation-presence effect and the honest one-vs-five null make it worth referee time. read the letter →

arxiv 2501.01303 v1 pith:4I36WYLW submitted 2025-01-02 cs.CL cs.AI

Yifan Ding , Matthew Facciani , Amrit Poudel , Ellen Joyce , Salvador Aguinaga , Balaji Veeramani , Sanmitra Bhattacharya , Tim Weninger This is my paper · ORCID
classification cs.CLcs.AI
keywords citationsusertrustlargelanguagemodelsanti-monitoringsocialproofretrieval-augmentedgenerationquestionansweringhuman-AIinteraction
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks whether showing sources in an AI chatbot's answer changes how much people trust the answer. In a live experiment with 303 participants, answers with citations received higher self-reported trust than answers without, and even citations chosen at random lifted trust. The number of citations did not matter: one and five produced similar ratings. The study also reports that participants who inspected citations by hovering over them gave lower trust ratings, consistent with the anti-monitoring view that checking is a sign of distrust. The authors conclude that citations function as social proof for AI-generated content, while the act of verification signals the absence of trust.

What carries the argument

The central machinery is the anti-monitoring theory of trust, paired with the Principle of Social Proof: trust is inferred from a reduction in surveillance, and citations act as visible endorsements that substitute for direct verification. The experiment operationalizes the theory by treating a mouse hover over a citation numeral as a monitoring event and a 1–10 rating as stated trust, then regressing trust on citation presence, citation relevance, and hover behavior while controlling for demographics. The load-bearing identity is the negative correlation between checking and trust: if hover frequency did not predict lower ratings, the anti-monitoring interpretation would lose its empirical support.

What would settle it

A replication that requires participants to click a citation to see the URL, and records click counts and dwell time instead of hovers, would test the anti-monitoring account: if click-based checking shows no negative relationship with trust, or if accidental hovers alone reproduce the effect, the claim that checking indicates lower trust would be falsified.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that trust in LLM-generated answers is driven more by the presence of a citation than by its quality: a randomized controlled trial with zero, one, or five citations, valid or random, found that 'has citation' significantly increased trust ratings, 'random citation' significantly decreased them relative to valid ones, and one versus five citations made no significant difference. A separate regression found that each citation check (mouse hover) was associated with significantly lower reported trust, and random citations that were checked lost the trust advantage entirely, being rated no better than answers with no citations. The authors interpret this asymmetry through trust as anti-monitoring: citations provide social proof that raises trust, while monitoring the citations indicates that trust is absent.

Load-bearing premise

The load-bearing assumption is that a mouse hover over a citation numeral counts as 'checking' the citation: hovering is effortless, can be accidental, and was not validated against deliberate inspection, yet the anti-monitoring conclusion depends entirely on that equation.

Editorial extensions

If this is right

  • Answers with any citation tend to be rated more trustworthy than identical answers with no citation, even when the cited sources are unrelated to the answer.
  • A single citation is as effective as five, so increasing citation count beyond one is unlikely to buy additional trust.
  • Users who inspect citations report lower trust, and random citations that get inspected lose their trust advantage, suggesting that checking neutralizes the social-proof effect.
  • Question content shifts trust: political and factual questions receive higher trust ratings, while more complex or longer prompts receive slightly lower ratings.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • An implication the authors do not draw: if random citations raise trust, then citations are functioning as a credibility cue rather than as verifiable evidence, which makes fabricated citations a direct trust-exploitation risk in deployed systems.
  • The one-versus-five null suggests users apply a binary 'is there a source' heuristic; a natural extension is to test whether citation count interacts with perceived source authority or domain risk.
  • Because hover conflates suspicion with curiosity, a click-to-reveal design would test whether the anti-monitoring result reflects distrust or mere exploration; it is a testable boundary condition on the paper's central claim.
  • The result that checked random citations lose the trust premium implies that transparency tools that surface citations may actually lower trust if the sources are weak, challenging the assumption that more transparency is always better.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. This paper reports a between-subjects randomized experiment (N=303; roughly 3,040 question-level ratings) in which participants asked a custom ChatGPT-based QA system ten questions and rated their trust in each answer. The system varied the number of citations (0, 1, or 5) and, for non-zero conditions, whether the citations were relevant to the answer or randomly drawn from previous participants' queries. The paper reports that citations increase self-reported trust, that random citations are rated lower than valid ones, that one and five citations do not differ, and that participants who hover over citations give lower trust ratings, which the authors interpret as support for trust-as-anti-monitoring. Additional exploratory analyses consider question type, demographics, and prompt perplexity.

Significance. If the presence-of-citations effect is robust, this is a useful empirical contribution to the literature on trust in LLM-generated content. The study's strengths include a live question-answering task with real user-generated questions, an experimental manipulation of citation count and relevance, and public availability of the data and Stata code. However, the headline claim that the trust increase 'held true even when the citations were random' is not supported by any direct statistical contrast in the reported analyses, the reported standard errors likely ignore participant-level clustering of repeated ratings, and the 'checking' measure is a mouse hover rather than a validated deliberate inspection. These issues are fixable with additional analyses and wording changes, but they currently limit the strength of the central conclusions.

major comments (3)
  1. [Abstract; Results, 'Does the Quality of Citation Matter?'; Table S2] The abstract and introduction claim that the trust increase 'held true even when the citations were random' (Abstract; Introduction). I could not find a direct test of random citations versus no citations. In Table S2, 'Has Citation' and 'Citation Random' enter the same model; the coefficient on 'Citation Random' (-0.268, SE 0.087) contrasts random with valid citations, not random with zero citations, because the zero-citation group is the omitted baseline for 'Has Citation'. To support the headline claim, report the joint contrast (Has Citation + Citation Random) with its standard error and p-value, or run an explicit random-versus-zero model on the full sample. The only direct comparison in the main text, in 'Does Checking Citations Indicate a Reduction in User Trust?', is restricted to the 193 checked questions and is nonsignificant (T=-0.877, p=0.38); this does not test the full-sample claim and in fact trends in the opposite direction.
  2. [Results, Tables S2, S3, and S4] The regressions and ANOVA treat the roughly 3,040 question-level ratings as independent observations, but each of the 303 participants contributes ten ratings in a single session. The between-subjects assignment of citation condition does not make these repeated ratings independent. The reported standard errors (e.g., Has Citation β=0.394, SE=0.0906 in Table S2) are therefore likely understated, and the p-values for the main effects may be too small. Please re-estimate the main models with cluster-robust standard errors at the participant level or with participant random effects, and report whether the presence-of-citations and checking effects remain significant.
  3. [Methodology; Results, 'Does Checking Citations Indicate a Reduction in User Trust?'; Table S4] The operational measure of 'checking' a citation is a mouse hover over the citation numeral, which reveals the URL but requires no click. The anti-monitoring conclusion ('checking citations decrease perceived trust') rests on treating hover as deliberate monitoring. Because hovers can be accidental or cursory, please validate the measure (e.g., click-through, dwell time, or a sensitivity analysis excluding very short hovers) or soften the claim to what the data support. In addition, Table S4 models Citation Checks as a function of Trust, so the data are consistent with lower trust leading to checking rather than checking causing lower trust; the abstract's phrasing 'decrease in self-reported user trust when participants checked the citations' should be revised to describe the associational direction actually tested.
minor comments (5)
  1. [Throughout] The model name 'ChatGPT4' should be 'ChatGPT-4' or 'GPT-4' for consistency with the cited technical report.
  2. [Results, 'Do Citations Increase User Trust?'] The first sentence contains a typo: 'In out initial analysis' should read 'In our initial analysis'.
  3. [Figures 2 and 4] The labels 'Participants Demographics SurveyResponse' (Fig. 2) and 'T rust' (Fig. 4) should be corrected for readability.
  4. [Supplement, 'Question Order, Citation Checking, and Trust'] The text says 'as illustrated in Table 6' but the item is a figure (Fig. 6); the cross-reference should be fixed.
  5. [Figure 3 caption] The caption states 'random citations decrease perceived trustworthiness'; this is only a decrease relative to valid citations, not relative to zero citations. Please state the reference category explicitly in the caption.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the trust and citation-checking claims are tested against independently collected behavioral data, not derived from the paper's own assumptions.

full rationale

This paper is an empirical randomized controlled trial, not a derivation. The hypotheses (citations increase trust; checking citations indicates lower trust) are operationalized through self-reported trust ratings and mouse-hover citation checks, and are then tested statistically against newly collected participant data. There is no equation in which a predicted quantity is constructed from the fitted inputs: the regression coefficients in Tables S2, S4, and S6 are estimates from data, not definitions, and the anti-monitoring framework is used only to motivate directional hypotheses rather than to compute trust values. No load-bearing self-citation appears: the one citation to the authors' prior work (Poudel and Weninger 2024) concerns question framing and does not establish any premise of the present experiment. The abstract's statement that the trust increase 'held true even when the citations were random' may be under-supported by the reported contrasts, but that is a statistical-evidence concern, not circularity, because the claim is not equivalent by construction to any fitted parameter or definition. The measuring of 'checking' as a hover over a citation numeral is a construct-validity limitation, not a circular reduction. Accordingly, no circular step is identifiable and the paper should be scored as non-circular.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper introduces no free parameters or invented entities; it relies on domain assumptions about the trust measure, hover as a checking proxy, sample representativeness, and live system stability.

assumptions (4)
  • domain assumption A one-item slider from 1 to 10 measures user trust in the answer.
    Used as the sole dependent variable; participants must move the slider from default 5, which may anchor responses.
  • domain assumption Mouse hover over a citation numeral operationalizes checking a citation.
    The anti-monitoring analysis depends on hover as a proxy for monitoring; not validated against clicks or self-report.
  • domain assumption Prolific participants are representative enough to generalize trust effects.
    Authors acknowledge the sample is tech-savvy and 65% white, limiting generalizability.
  • domain assumption ChatGPT4 and Google search results are stable across the experiment.
    The live system may produce different outputs for the same question at different times; no version pinning is provided.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Citations and Trust in LLM Generated Responses." pith.science (2026). https://pith.science/paper/4I36WYLW

@misc{pith2026250101303,
  author       = {Pith},
  title        = {Pith review of: Citations and Trust in LLM Generated Responses},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4I36WYLW}},
  note         = {Machine review of arXiv:2501.01303}
}
read the original 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.

Figures

Figures reproduced from arXiv: 2501.01303 by the authors.

Figure 1
Figure 1. AI Chatbot system answering a user’s question [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Methodology of the Citation Trust Experiment. Participants are assigned to zero ( [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Citations increase perceived trustworthiness, but [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Checking citations decrease perceived trust. Po- Trust [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Visualization of question-topics asked by partic [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Number of Citation Checks per Question. The frequency that a citation is checked is not correlated with the question [PITH_FULL_IMAGE:figures/full_fig_p014_6.png]
Figure 7
Figure 7. Figure 7: Trust ratings as a function of offset from a citation check. Gray lines represent individual participant trust ratings [PITH_FULL_IMAGE:figures/full_fig_p014_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

53 extracted references · 37 canonical work pages

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

  3. [3]

    L.; Almeida, D.; Altenschmidt, J.; Altman, S.; Anadkat, S.; et al

    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 2023

  4. [4]

    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

    work page 2023

  5. [5]

    Aoki, N. 2020. An experimental study of public trust in AI chatbots in the public sector. Government information quarterly, 37(4): 101490

    work page 2020

  6. [6]

    A.; and Weinstock, D

    Archard, D.; Deveaux, M.; Manson, N. A.; and Weinstock, D. M. 2013. Reading Onora O'Neill. Routledge London/New York, NY

    work page 2013

  7. [7]

    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 2023

  8. [8]

    W.; Zettlemoyer, L.; Hajishirzi, H.; and Yih, W.-t

    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 2024

Show all 53 references
  1. [9]

    Azaria, A.; and Mitchell, T. 2023. The internal state of an llm knows when its lying. arXiv preprint arXiv:2304.13734

    2023 arXiv

  2. [10]

    Baier, A. 1986. Trust and Antitrust. Ethics, 96(2): 231--260

    1986

  3. [11]

    Bonferroni, C. 1936. Teoria statistica delle classi e calcolo delle probabilita. Pubblicazioni del R istituto superiore di scienze economiche e commericiali di firenze, 8: 3--62

    1936

  4. [12]

    Castelo, N.; and Ward, A. F. 2021. Conservatism predicts aversion to consequential Artificial Intelligence. Plos one, 16(12): e0261467

    2021

  5. [13]

    M.; and Zhu, H

    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

    2019

  6. [14]

    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

    2023

  7. [15]

    Cialdini, R. 2009. Social proof: Truths are us. Influence: Science and practice, 97--140

    2009

  8. [16]

    C.; Parkinson, M.; and Aquino, K

    Claudy, M. C.; Parkinson, M.; and Aquino, K. 2024. Why should innovators care about morality? Political ideology, moral foundations, and the acceptance of technological innovations. Technological Forecasting and Social Change, 203: 123384

    2024

  9. [17]

    H.; Liu, B

    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

    2019

  10. [18]

    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 Proceedings of the 24th international conference on intelligent user interfaces, 263--274

    2019

  11. [19]

    Ferrario, A.; and Loi, M. 2022. How Explainability Contributes to Trust in AI . In 2022 ACM Conference on Fairness , Accountability , and Transparency , 1457--1466. Seoul Republic of Korea: ACM. ISBN 978-1-4503-9352-2

    2022

  12. [20]

    A.; and Zettlemoyer, L

    Gonen, H.; Iyer, S.; Blevins, T.; Smith, N. A.; and Zettlemoyer, L. 2022. Demystifying prompts in language models via perplexity estimation. arXiv preprint arXiv:2212.04037

    2022 arXiv

  13. [21]

    A.; and Bashir, M

    Hoff, K. A.; and Bashir, M. 2015. Trust in automation: Integrating empirical evidence on factors that influence trust. Human factors, 57(3): 407--434

    2015

  14. [22]

    D.; Kessler, T

    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

    2023

  15. [23]

    J.; Choi, J

    Kim, Y. J.; Choi, J. H.; and Fotso, G. M. N. 2024. Medical professionals' adoption of AI-based medical devices: UTAUT model with trust mediation. Journal of Open Innovation: Technology, Market, and Complexity, 10(1): 100220

    2024

  16. [24]

    u ttler, H.; Lewis, M.; Yih, W.-t.; Rockt \

    Lewis, P.; Perez, E.; Piktus, A.; Petroni, F.; Karpukhin, V.; Goyal, N.; K \"u ttler, H.; Lewis, M.; Yih, W.-t.; Rockt \"a schel, T.; et al. 2020. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems, 33: 9459--9474

    2020

  17. [25]

    Lins, S.; and Sunyaev, A. 2023. Advancing the presentation of IS certifications: theory-driven guidelines for designing peripheral cues to increase users’ trust perceptions. Behaviour & Information Technology, 42(13): 2255--2278

    2023

  18. [26]

    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

    2018

  19. [27]

    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

    2011

  20. [28]

    McInnes, L.; Healy, J.; and Melville, J. 2018. Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426

    2018 arXiv

  21. [29]

    Miller, D.; Johns, M.; Mok, B.; Gowda, N.; Sirkin, D.; Lee, K.; and Ju, W. 2016. Behavioral measurement of trust in automation: 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

    2016

  22. [30]

    Miller, T. 2019. Explanation in artificial intelligence: Insights from the social sciences. Artificial intelligence, 267: 1--38

    2019

  23. [31]

    K.; Sharma, A.; and Tan, C

    Mothilal, R. K.; Sharma, A.; and Tan, C. 2020. Explaining machine learning classifiers through diverse counterfactual explanations. In Proceedings of the 2020 conference on fairness, accountability, and transparency, 607--617

    2020

  24. [32]

    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

    1987

  25. [33]

    Nothdurft, F.; Heinroth, T.; and Minker, W. 2013. The impact 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, Pa...

    2013

  26. [34]

    Palan, S.; and Schitter, C. 2018. Prolific. ac—A subject pool for online experiments. Journal of Behavioral and Experimental Finance, 17: 22--27

    2018

  27. [35]

    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

    2022

  28. [36]

    Poudel, A.; and Weninger, T. 2024. Navigating the Post-API Dilemma. In Proceedings of the ACM on Web Conference 2024, 2476--2484

    2024

  29. [37]

    G.; Hofman, J

    Poursabzi-Sangdeh, F.; Goldstein, D. G.; Hofman, J. M.; Wortman Vaughan, J. W.; and Wallach, H. 2021. Manipulating and measuring model interpretability. In Proceedings of the 2021 CHI conference on human factors in computing systems, 1--52

    2021

  30. [38]

    Rainie, L.; Funk, C.; Anderson, M.; and Tyson, A. 2022. How Americans think about artificial intelligence. Pew Research Center: Washington, DC, USA

    2022

  31. [39]

    B.; Sadler, B

    Rawal, A.; McCoy, J.; Rawat, D. B.; Sadler, B. M.; and Amant, R. S. 2021. Recent advances in trustworthy explainable artificial intelligence: Status, challenges, and perspectives. IEEE Transactions on Artificial Intelligence, 3(6): 852--866

    2021

  32. [40]

    Reimers, N.; and Gurevych, I. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics

    2019

  33. [41]

    Rosen, J. 1999. What are journalists for? Yale University Press

    1999

  34. [42]

    Sambrook, R. 2012. Delivering trust: Impartiality and objectivity in the digital age. Reuters Institute for the Study of Journalism

    2012

  35. [43]

    A.; Svab, M.; Opwis, K.; and Br \"u hlmann, F

    Scharowski, N.; Perrig, S. A.; Svab, M.; Opwis, K.; and Br \"u hlmann, F. 2023. Exploring the effects of human-centered AI explanations on trust and reliance. Frontiers in Computer Science, 5: 1151150

    2023

  36. [44]

    Srinivas, A.; and Friedman, L. 2024. Aravind Srinivas: Perplexity CEO on Future of AI, Search & the Internet Lex Fridman Podcast \#434. https://www.youtube.com/watch?v=e-gwvmhyU7A

    2024

  37. [45]

    Y.; Dong, J.; Duffy, V

    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. International Journal of Human--Computer Interaction, 35(14): 1229--1269

    2019

  38. [46]

    Sun, Z.; Wang, X.; Tay, Y.; Yang, Y.; and Zhou, D. 2022. Recitation-augmented language models. arXiv preprint arXiv:2210.01296

    2022 arXiv

  39. [47]

    R.; Herman, E.; Allard, S.; Levine, K.; and Tenopir, C

    Thornley, C.; Watkinson, A.; Nicholas, D.; Volentine, R.; Jamali, H. R.; Herman, E.; Allard, S.; Levine, K.; and Tenopir, C. 2015. The role of trust and authority in the citation behaviour of researchers. Information research

    2015

  40. [48]

    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

    2023 arXiv

  41. [49]

    Tyson, A.; and Kikuchi, E. 2023. Growing public concern about the role of artificial intelligence in daily life. Technical report

    2023

  42. [50]

    Venkatesh, V.; and Davis, F. D. 2000. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2): 186--204

    2000

  43. [51]

    Wachter, S.; Mittelstadt, B.; and Russell, C. 2017. Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harv. JL & Tech., 31: 841

    2017

  44. [52]

    V.; and Bellamy, R

    Zhang, Y.; Liao, Q. V.; and Bellamy, R. K. 2020. Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making. In Proceedings of the 2020 conference on fairness, accountability, and transparency, 295--305

    2020

  45. [53]

    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 Intelligent Systems and Technology, 15(2): 1--38

    2024

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.