{"id":"78d7fca1-2554-4199-b856-2f7be54ed8f6","arxiv_id":"2504.16883","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"high","formal_verification":"none","parameter_count":0,"one_line_summary":"A pilot study of 18 participants reports that tailored warning messages improve hallucination detection in a RAG-based quiz, but the tiny sample, absent data, and potentially answer-revealing warnings weaken the evidence.","lead":"RAG systems supplement large language models with external facts, but the AI can still generate false or biased content. This paper reports a small 18-participant pilot testing whether customized warning messages, tailored to the specific error, help people spot hallucinations in a history quiz, and finds accuracy improvements alongside signs of user confusion.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Tailored warnings may reveal the correct answer rather than train critical reasoning; the accuracy gains cannot separate answer leakage from improved hallucination detection.","rationale":"The reader's weakest_assumption identifies the same confound: tailored warnings derived from the specific problems in the LLM output may contain the correct answer or strong hints. I agree this is the most load-bearing concern. The manuscript provides no example of the actual warning text and no condition that controls for information content, so the accuracy improvement cannot be causally attributed to improved critical thinking. The participant quote in Section 5 is direct evidence that at least some users perceived the warnings as almost providing the answer. Because the central claim rests on this causal interpretation, the paper's conclusion is unsupported as written. The lack of data, small sample, and missing trust statistics are additional weaknesses, but the answer-revelation confound alone is sufficient to reject the strongest claim.","tokens_in":4575,"tokens_out":2133,"duration_ms":22040,"concrete_test":"Run a controlled follow-up on the same 18 items with three warning conditions: (1) the current tailored warnings; (2) matched reflection-only warnings that identify the type and location of the potential error without stating the correct fact, e.g., 'Check whether the date and treaty name in the answer match the textbook excerpt'; (3) no warning. Compare accuracy between conditions (2) and (3). If reflection-only warnings do not improve accuracy over no warning, the current tailored-warning gains are attributable to answer revelation rather than to enhanced critical reasoning.","verdict_should_be":"REJECT","load_bearing_attack":"The central claim is that tailored warnings enhance hallucination detection, but the pilot's tailored warning is generated from the specific problems in the LLM's output (Section 3). Such warnings can contain the correct answer or strong hints, making the measured accuracy gains (100%/89%/81% vs. 97%/81%/69% vs. 100%/64%/56%) impossible to attribute to improved critical thinking. The paper's own qualitative data expose this: a participant asks, 'Why can't you just provide us the right answer if you know how to warn us?' (Section 5). No control condition separates warnings that reveal the correct answer from warnings that only prompt reflection or verification. Because the dependent variable is quiz accuracy rather than a direct measure of hallucination detection, the observed improvement is equally consistent with answer revelation. This is not a mere sample-size concern: it is a confound that undermines the causal interpretation of the headline result.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The manuscript proposes a tailored warning system for retrieval-augmented generation (RAG) models, in which a fact-check is performed at both the retrieval and generation stages and a warning message is generated from the specific problems in the LLM output. The authors report a pilot study with 18 participants split into no-warning, standard-warning, and tailored-warning conditions, who answered 18 multiple-choice questions with responses containing no, low, or high levels of hallucination. They report higher answer accuracy in the tailored-warning group (100%/89%/81% for no/low/high hallucination, respectively) and an ANOVA p-value of 0.006, along with survey measures of trust and usability. The paper argues that tailored warnings improve users' ability to detect hallucinations and support critical thinking, while also noting cognitive friction and user resistance in the qualitative data.","tokens_in":4863,"tokens_out":4043,"duration_ms":38436,"significance":"The topic is timely and important for human-AI interaction: understanding how user-facing warnings can help people engage critically with RAG outputs is a genuine open problem. The paper's strengths include a clear experimental setup, a mixed-methods design combining accuracy, Likert-scale measures, and interview comments, and an honest acknowledgment in the discussion that warnings can confuse users and reduce trust. If the causal claim were supported, the paper would provide a useful contribution to the design of AI-augmented reasoning systems. However, the current evidence is preliminary and the central comparison is confounded; the reported effect is equally consistent with the tailored warning revealing the correct answer rather than improving critical reasoning.","major_comments":[{"comment":"The tailored warning is generated from the specific problems in the LLM's output statement, so it can contain the correct answer or strong hints toward it. The observed accuracy differences (100%/89%/81% vs. 97%/81%/69% vs. 100%/64%/56%) are therefore compatible with answer revelation rather than improved hallucination detection or critical thinking. The participant's comment in Section 5, 'Why can't you just provide us the right answer if you know how to warn us?', directly supports this concern. No control condition separates a warning that reveals the answer from a warning that merely prompts reflection or verification, so the central claim that tailored warnings enhance users' ability to detect hallucinations is not supported by the study design.","section":"Section 3, Section 4, Section 5"},{"comment":"The ANOVA p-value of 0.006162 is reported without stating the unit of analysis (participant-level with n=6 per group, or question-level with repeated measures per participant), without checking ANOVA assumptions (normality, homogeneity of variance, independence), and without reporting effect sizes or confidence intervals. Because each participant answered all 18 questions, question-level observations are non-independent; participant-level analysis would have only 6 observations per group. The claim that 'despite the small sample size, this strongly suggests that the results are statistically significant' is not justified by the information provided.","section":"Section 4"},{"comment":"The dependent variable throughout the accuracy analysis is multiple-choice quiz accuracy, not a direct measure of hallucination detection or critical thinking. A participant may select the correct answer by relying on a warning that states the correction, without engaging in the reasoning the paper claims to enhance. The manuscript lacks any measure of whether participants actually identified the hallucinated content (e.g., asking them to flag or explain the error), so the accuracy results cannot be interpreted as evidence of improved detection.","section":"Section 4, Section 5"},{"comment":"The trust and usability findings are presented as meaningful but are statistically unsupported. The paper reports that the tailored group had a trust difference of 0.67 on a 5-point Likert scale, and Section 5 calls this 'statistically significant,' but no statistical test, p-value, or confidence interval for the trust or ease-of-use measures is reported in Section 4. With six participants per condition, this difference may be well within sampling variation, and the manuscript's interpretation overstates the evidence.","section":"Section 4, Section 5"}],"minor_comments":[{"comment":"The abstract appropriately says 'preliminary findings suggest,' but Section 4 states 'These results demonstrates that tailored warnings substantially enhance participants' ability to detect hallucinations.' Please align the language with the pilot nature of the study and correct the subject-verb agreement.","section":"Abstract, Section 4"},{"comment":"The description 'We divided eighteen questions into three groups of six' is ambiguous given that all participants received the same 18 questions. Please clarify that each participant answered all 18 items and that the hallucination level was manipulated within subjects.","section":"Section 3"},{"comment":"Figure 2 is described as a heatmap of accuracy rates, but the axes and cell values are not described in the text. Please make the figure self-contained with labeled axes and a color scale.","section":"Figure 2"},{"comment":"The trust and ease-of-use figures report only group means without showing individual data, error bars, or distributions. With only six participants per group, dot plots or box plots would be more informative and would help the reader assess the variability.","section":"Figure 3, Figure 4"},{"comment":"Several references appear incomplete or inconsistently formatted, e.g., reference [2] truncates an author name as 'Han.' and reference [10] is labeled with a different method name in the text ('RAG-end2end') than in the citation. Please check the reference list against the text.","section":"References"},{"comment":"The sentence 'The statistically significant difference in trust levels (0.67 on a 5-point scale)' conflates a mean difference with a statistical result; the paper should report the relevant test statistic and p-value in Section 4 if it wants to claim significance.","section":"Section 5"}],"recommendation":"reject","confidential_remarks":"The paper reads as an early-stage workshop position rather than a complete empirical contribution. The central causal claim is confounded by the tailored warning potentially revealing the correct answer, and the statistical analysis is under-specified. While the topic is well suited to the HCI community, the current manuscript would require new data from a redesigned control condition to support its claims; a text-only revision cannot repair the internal validity issue. I would recommend the authors reposition this as a design exploration or pilot reporting preliminary observations, rather than as evidence of enhanced critical thinking."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"The paper asks a good question—whether warning messages tailored to the specific failure mode in a RAG pipeline help users spot hallucinations—but the pilot as designed cannot answer it, because the tailored warning is constructed from the very hallucination it warns about and may simply reveal the correct answer.\n\nWhat's new and good: the three-condition comparison (no warning vs. generic vs. tailored) in a RAG educational setting is a natural extension of Nahar et al.'s \"Fakes of Varying Shades\" and is worth testing. The paper is transparent about being preliminary, and the qualitative comments (users asking why the system doesn't just give the right answer) are genuinely useful for anyone designing such interventions.\n\nSoft spots: the core issue is answer leakage. The warning is generated from the specific problems in the LLM's output, so it can contain the correct answer or strong hints. The dependent variable is multiple-choice accuracy; a participant who follows the warning may simply be selecting the answer the warning points to, not engaging in deeper reasoning. That is a direct confound with the title's claim about critical thinking. The paper itself includes a participant who says exactly this. Without a control condition matching warnings that prompt reflection but do not reveal the answer, the 100%/89%/81% accuracy figures cannot be attributed to enhanced hallucination detection.\n\nThe statistics are also not load-bearing. Eighteen participants, six per condition, with each answering 18 questions gives non-independent observations; ANOVA on group means without checking assumptions, confidence intervals, or effect sizes is not convincing. The trust result is mentioned but no test statistic is reported. The paper doesn't provide data or materials, so the results can't be checked.\n\nWho it's for: this is a workshop-level position paper. It could be a useful starting point for a properly designed study. As an empirical claim, it doesn't support the headline.\n\nRecommendation: I would not send this to peer review in its current form. The confound is fixable: separate warnings that reveal the answer from warnings that only prompt verification, measure hallucination detection directly (e.g., ask participants to label each answer as hallucinated before making a decision), pre-register, and collect enough independent responses. If the authors do that, the question deserves a serious journal. For now, desk-reject or send back to the workshop.","headline":"Tailored warnings in this pilot likely leak the correct answer, so the accuracy gains can't be separated from answer revelation—a promising idea that needs a properly controlled study.","tokens_in":5200,"tokens_out":2657,"would_cite":false,"duration_ms":24382,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Tailored warning messages—not generic disclaimers—help people detect hallucinations in RAG-based answers.","keywords":["Retrieval-Augmented Generation","hallucination detection","tailored warning messages","human-AI interaction","AI-augmented reasoning","user trust","critical thinking","educational quiz"],"falsifier":"Run the same 18-question quiz with a fourth group receiving a tailored warning that says only 'This answer contains a factual error; find it and explain why'—with no hint about which claim is wrong. If that reflection-only warning performs no better than the generic warning, the tailored condition's advantage is answer leakage; if it matches, the cognitive-scaffold explanation survives.","tokens_in":4411,"feed_emoji":"⚠️","tokens_out":9480,"duration_ms":84397,"temperature":0.7,"pith_summary":"This paper argues that warning messages tailored to the specific error in a retrieval-augmented generation (RAG) system—a system that retrieves documents and feeds them to a language model—can improve how well people detect false information. In an eighteenth-century history quiz built from a textbook, participants who received tailored warnings answered correctly in 89% of low-level hallucination cases and 81% of high-level hallucination cases, compared with 81% and 69% for a generic warning and 64% and 56% for no warning. The authors report an ANOVA p-value of 0.006, suggesting the accuracy differences are unlikely to be chance even with only 18 participants. They also find that tailored warnings raised self-reported trust in the system by 0.67 on a 5-point scale, but some participants said the warnings confused them and asked why the system did not just give the right answer. The point of the work is that user-facing feedback should move from generic disclaimers to context-specific cognitive support.","feed_headline":"Tailored AI warnings beat generic ones on false-answer detection","feed_subtitle":"In a quiz test, tailored warnings catch 81 percent of high-level hallucination cases, vs 69 for generic.","key_machinery":"The central mechanism is the tailored warning message: an alert produced by placing a 'fact-check' at both the retrieval stage and the LLM-output stage, with the warning text derived from the specific context of the problem found at either layer. In the quiz, participants in the treatment group receive this context-specific alert; the standard and no-warning groups receive a generic disclaimer or nothing. The warning is designed as a cognitive scaffold rather than an answer key, and the authors' thesis is that this contextual specificity is what lets users detect hallucinated content and calibrate trust.","core_discovery":"The paper's central claim is that a warning whose content depends on why the retrieved or generated text is wrong—not a fixed disclaimer—helps users detect hallucinations and calibrate trust in a RAG-based educational assistant. The authors compare three conditions on the same eighteen quiz items: no warning, a standard 'ChatGPT can make mistakes' style notice, and a tailored warning generated from the specific problems in the LLM's output statement. Tailored warnings produced the highest accuracy at every hallucination level and the highest self-reported trust. The authors interpret this as evidence that context-specific warnings act as cognitive scaffolds, guiding reflective evaluation instead of passive acceptance. They also report cognitive friction: some participants said the warnings confused them or wanted the correct answer directly.","pith_inferences":["The paper does not separate warnings that reveal the answer from warnings that only prompt reflection; a reflection-only condition would locate whether the benefit is cognitive or informational.","Because the tailored warning is generated from the detected problem, a natural product design would be to let users choose how much detail the warning reveals—hint versus reason—which the data hint at but do not test.","The trust increase may be caused by the warning acting as a sign of competence, not just a sign of danger; treating trust as a separate outcome could matter for deployment beyond accuracy."],"forward_implications":["RAG-based tutors could include context-specific warnings as a standard interface element rather than a static disclaimer.","Higher trust alongside better detection suggests transparent feedback does not have to make users distrust the system.","The largest accuracy gain appears exactly where generic warnings fail: high-level hallucinations (81% vs. 69% vs. 56%).","The participant discomfort reported in the paper implies that warning design must balance reflection support against users' preference for direct answers, or the friction will limit adoption."],"supporting_citations":[{"why":"Documents that unreliable retrieval sources can propagate incorrect information, setting up the retrieval-level hallucination the warnings target.","marker":"[2]"},{"why":"Shows an iterative RAG verification method for fact-checking, a precedent for detecting errors before the user sees them.","marker":"[5]"},{"why":"Introduces a question-answer restructuring approach for RAG, related design context for the educational quiz interface.","marker":"[7]"},{"why":"Provides the prior evidence that warnings can change how people perceive and engage with LLM hallucinations, the baseline this study extends.","marker":"[8]"},{"why":"Establishes that textbooks carry bias tied to publication time and place, motivating the history-quiz testbed.","marker":"[9]"},{"why":"Raises the fairness concern that RAG can inject bias during generation, the second hallucination layer addressed by the warning.","marker":"[16]"},{"why":"Shows a proactive risk-assessment approach to AI tool design, supporting the idea that warnings belong in early user-facing design.","marker":"[14]"}],"fun_headline_variants":["Tailored AI warnings catch 81% of false answers","Custom warnings beat generic for spotting AI mistakes","Context-aware AI cautions improve hallucination detection","Personalized AI alerts sharpen error spotting"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The tailored warnings are built from the specific problems in the LLM's output, so they may reveal the correct answer or strong hints; if that is what drives the higher accuracy, the improvement would reflect answer revelation rather than improved critical reasoning.","fun_headline_variants_meta":{"raw":{"variants":["Tailored AI warnings catch 81% of false answers","Custom warnings beat generic for spotting AI mistakes","Context-aware AI cautions improve hallucination detection","Personalized AI alerts sharpen error spotting"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000204,"raw_usage":{"total_tokens":1332,"prompt_tokens":831,"completion_tokens":501,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":447,"completion_tokens_details":{"reasoning_tokens":443}},"tokens_in":447,"tokens_out":501,"duration_ms":5190,"temperature":1.0,"reasoning_tokens":443,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-16T10:52:21.065054+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run the same 18-question quiz with a fourth group receiving a tailored warning that says only 'This answer contains a factual error; find it and explain why'—with no hint about which claim is wrong. If that reflection-only warning performs no better than the generic warning, the tailored condition's advantage is answer leakage; if it matches, the cognitive-scaffold explanation survives.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Introduces a question-answer restructuring approach for RAG, related design context for the educational quiz interface."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Establishes that textbooks carry bias tied to publication time and place, motivating the history-quiz testbed."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Shows a proactive risk-assessment approach to AI tool design, supporting the idea that warnings belong in early user-facing design."}],"review_version":1}