{"id":"2a05b86e-712d-4af4-a6fa-75f1281cb4df","arxiv_id":"2507.10124","paper_version":1,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":4.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"Asking an LLM 'could you be wrong?' after its answer surfaces its own biases, omitted evidence, and alternative perspectives in qualitative demonstrations on three tasks.","lead":"A short prompt asking an LLM 'could you be wrong?' after its first answer makes the model generate caveats, biases, and counter-evidence it had not mentioned. The paper shows three worked examples with ChatGPT-4o and claims the same for three other models.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The paper demonstrates self-critical commentary, but not debiasing: no evidence that the prompt changes LLM answers or improves decisions.","rationale":"The paper is a short demonstration, not a systematic study, and as a demonstration of 'self-critical text elicitation' it is plausible: the transcripts show ChatGPT-4o producing reflective commentary after 'could you be wrong?' The author is transparent about the format and the repeated-run claim, though those runs are not logged. The reader's concern about the lack of causal specificity against generic prompts is valid, and my concern is adjacent but more central: even if the prompt is uniquely responsible for the commentary, the paper never shows that this commentary reduces bias in the model's decisions. In each transcript the initial answer stands uncorrected; the critique is append-only. The discussion concludes 'effective tool for debiasing,' which requires evidence that the model's subsequent behavior or the user's decision improves. This is a missing measurement, not a theoretical inconsistency, so the appropriate response is to condition acceptance on that measurement rather than to reject the paper outright. The proposed test directly assesses whether revised answers become less biased and whether the effect exceeds a generic elaboration control, which would separate metacognitive debiasing from simple 'talking more' about an answer. If the test failed, the paper's central claim would overreach; if it passed, the paper would become a solid empirical contribution.","tokens_in":7215,"tokens_out":2744,"duration_ms":34596,"concrete_test":"Run the three tasks (Bai et al. word association, Griot et al. fictional-disease MCQ, too-much-choice factual question) on N=100 prompts per task with ChatGPT-4o under default settings. For each prompt, collect (a) the initial answer, (b) the response to 'could you be wrong?', and (c) a final revised answer: 'Given your critique, what is your final answer?' Score (a) and (c) with the same bias/accuracy metrics (e.g., implicit bias association score, correctness of 'none of the above', inclusion of replication controversy). Compare (a) vs (c), and also compare the 'could you be wrong?' condition against a control follow-up 'Please elaborate on your answer.' If the revised answers do not show significantly less bias than initial answers, or if they are not significantly better than the 'elaborate' control, the debiasing claim is unsupported.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The paper's central claim is that 'could you be wrong?' is 'a practical and effective tool for debiasing ... LLMs' (Discussion). The evidence, however, demonstrates only that the prompt elicits additional self-critical text; it never shows that this text debiases the LLM's outputs. In all three figures, the initial response is left as the final answer; the critique is appended but the model is never asked to revise or corrected. The abstract's own claim is carefully limited ('produces additional information... none of which were apparent in their initial response'), which is consistent with the transcripts. But 'debiasing' requires that the bias in the model's decisions is reduced—that subsequent answers change or that human decisions improve. The paper contains no quantitative bias metric, no revised-answer comparison, and no downstream decision-quality measurement. Without such evidence, the demonstrated phenomenon is 'LLMs can produce metacognitive commentary when asked,' not 'LLMs are debiased by this prompt.' The concern is load-bearing because the title and Discussion stake the paper's value on the debiasing claim, not merely on eliciting reflection.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper argues that a metacognitive follow-up prompt from the human decision-making literature, 'could you be wrong?', can debias large language models by eliciting self-critical information. Drawing on human debiasing strategies (e.g., dialectical bootstrapping and premortems), the author applies the prompt after ChatGPT-4o's responses to three tasks: a word-association test of implicit bias from Bai et al. (2025), a medical multiple-choice question about a fictional organ from Griot et al. (2025), and a request to summarize the 'too much choice' effect. The three verbatim transcripts show that the model produces additional text identifying biases, counter-evidence, and alternatives. The paper concludes that the prompt is 'a practical and effective tool for debiasing both humans and, as demonstrated here, LLMs,' without quantifying bias reduction, comparing against control prompts, or testing downstream decisions.","tokens_in":7419,"tokens_out":5768,"duration_ms":61162,"significance":"The proposed transfer of metacognitive prompting from human judgment research to LLMs is original and potentially useful, and the paper is commendably transparent: it specifies the model, date, and settings, reports multiple runs, and provides verbatim excerpts, including a sanity check with a novel fictional disease. The phenomenon demonstrated—that follow-up questioning can make LLMs articulate limitations of their own answers—is real and worth reporting. However, the significance as a debiasing result is currently unestablished: the transcripts show appended self-critique, not reduced bias in outputs or improved human decisions. The paper is a proof-of-concept for prompt-elicited self-criticism, not evidence for the debiasing claim made in the title, abstract, and Discussion.","major_comments":[{"comment":"The Discussion's claim that 'could you be wrong' is 'a practical and effective tool for debiasing both humans and, as demonstrated here, LLMs' is not supported by the data in Figures 1–3, because none of the demonstrations measures a reduction in bias. In each figure the model's initial answer is left untouched, and the follow-up prompt produces only additional self-critical text; the paper never asks the model to revise its answer or compares pre- and post-critique outputs on any bias metric. Debiasing requires showing that the biased behavior changes, so the experiments as presented establish only that LLMs can generate metacognitive commentary when prompted. I recommend either reframing the core claim to 'elicits self-critical information' or adding a revision step and a quantitative bias measurement.","section":"Discussion; Figures 1–3"},{"comment":"The causal role of the metacognitive phrasing is untested because the paper lacks any control prompt. The follow-up always uses 'could you be wrong?', so the additional text could be elicited by any generic request for elaboration, such as 'please elaborate' or 'what are some caveats?'. In addition, the manuscript states that each case was run 'multiple times (>10)' with 'qualitatively consistent responses' across four models, but no data or negative examples are provided, and the author shows only a single transcript per case. Adding a control condition and a systematic summary of all runs, including any failures, is necessary to support the attribution of the effect to metacognitive prompting and to rule out selective reporting.","section":"Discriminatory bias: Bai et al., 2025; Figures 1–3"},{"comment":"The claimed improvements in metacognition and factual accuracy are not quantified. In the Glianorex/Clapzym Morphistic Disorder example, the model still selects option A even after acknowledging that the condition is fictional, so its behavior on the benchmark's central criterion is unchanged; in the too-much-choice example, ChatGPT does not correct its initial summary but only adds caveats after prompting. The paper reports no error rates, no calibration scores, no revised-answer comparison, and no human-subject evaluation of decision quality, so the abstract's promise of 'improving human decision making' is unmeasured.","section":"Metacognitive bias: Griot et al. 2025; Evidence omission: The too much choice effect"}],"minor_comments":[{"comment":"The word 'disciminatory' appears in the sentence 'I apply this prompt following a set of prompts that have been used to reveal disciminatory bias'; it should be 'discriminatory'.","section":"Introduction, final paragraph before 'Discriminatory bias'"},{"comment":"In the paragraph beginning 'The author's argue that not choosing...', the correct plural is 'The authors argue'; the current phrasing contains a typographical error.","section":"Metacognitive bias: Griot et al. 2025"},{"comment":"In the first paragraph, 'enahnce' should be 'enhance' in 'how a metacognitive prompt can enahnce an LLM's capacity'.","section":"Evidence omission: The too much choice effect"},{"comment":"The manuscript uses 'LLM's' as a plural in several places (e.g., 'LLM's often respond to prompts with stereotypical and popular responses'); the correct plural is 'LLMs'.","section":"Throughout"},{"comment":"The text notes that only the beginning of the model's response is shown, but the figure caption implies the full chat thread is displayed; consider adding the complete response as supplementary material for verification.","section":"Figure 2"},{"comment":"The claim that the novel fictional disease check rules out training-data leakage is too strong; a more convincing test would include several held-out fictional terms and repeated generations.","section":"Metacognitive bias: Griot et al. 2025"}],"recommendation":"major_revision","confidential_remarks":"This is a transparently reported proof-of-concept that would benefit from a strict framing adjustment. The core observation is interesting, but the current wording overstates the evidence. If the author can add a small quantitative study (e.g., multiple models and prompts with a generic control, measuring whether the final answer changes or whether a bias score drops), the paper could make a meaningful contribution. As it stands, I would not accept without those changes. No concerns about citation practice or novelty disclosure."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Quick read on arXiv:2507.10124. The prompt 'could you be wrong?' does elicit genuinely useful self-critical text from ChatGPT-4o in the three demonstrated cases: biased word associations, a fictional-disease medical question, and the too-much-choice effect. The transcripts are verbatim, the tasks are drawn from recent real papers, and the connection to the human dialectical bootstrapping literature is legitimate and well sourced. That is the actual contribution, and it is real but narrow.\n\nWhat the paper does not do is show debiasing. The initial answers are never revised; the critique is appended. There is no measure of whether the final output or downstream human decision improves. The title and discussion call it 'a practical and effective tool for debiasing... LLMs,' which overreaches the evidence. The abstract is more carefully worded; the discussion is not. The stress-test note is right on this.\n\nSoft spots, in order of severity. First, no baseline comparison. A generic follow-up like 'please elaborate' or 'any caveats?' might produce similar self-critique, and nothing here shows 'could you be wrong?' is special. Second, the repeated-runs claim (>10 per case) is unverifiable: no logs, no counts, no counter-examples. Third, the paper does not engage the existing LLM self-refinement literature—Self-Refine, reflexion, self-consistency—so the novelty is oversold. Fourth, the human literature's dialectical bootstrapping aggregates two judgments because the second judgment improves accuracy; here there is no second judgment and no aggregation, so the psychology bridge is suggestive, not demonstrative.\n\nNone of this makes the paper worthless. As a short demonstration it is honest about what is shown—'produces additional information'—and the cases are well chosen. The right outcome: send to peer review as a short report, requiring the authors to either correct the framing to 'eliciting self-critical commentary' or add a small quantitative evaluation, such as one revised-answer condition or a baseline prompt comparison. I would not desk-reject; I would referee it with expectation of major revision or re-scope.","headline":"The paper shows a simple metacognitive prompt can elicit self-critical LLM commentary, but it does not demonstrate debiasing; as a short demonstration it deserves a serious referee with a re-scope or added evidence.","tokens_in":7915,"tokens_out":2441,"would_cite":false,"duration_ms":25480,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Asking an LLM 'could you be wrong?' makes it reveal its own biases and omitted evidence.","keywords":["large language models","metacognitive prompting","debiasing","implicit bias","epistemic humility","self-critique","choice overload","prompt engineering"],"falsifier":"Run the same initial questions with generic follow-up prompts, such as 'please elaborate', 'any caveats?', or 'give me more detail', and score whether they elicit the same bias acknowledgements, unanswerability admissions, and counterevidence; if the generic prompts do, the metacognitive wording is not the causal ingredient. Also measure whether the model's answer to the original question changes after the follow-up, which the paper does not report.","tokens_in":7009,"feed_emoji":"🤔","tokens_out":8379,"duration_ms":79608,"temperature":0.7,"pith_summary":"This paper asks whether a technique used to debias human decision makers—prompting people to consider why their judgment might be wrong—also works on large language models. The author applies the follow-up prompt 'could you be wrong?' to three known LLM failures: implicit discriminatory associations, inability to admit unanswerable medical questions, and confident summaries that omit counter-evidence. In each case, the prompt elicits a second response that names the bias, acknowledges the missing information, or surfaces the contradictory evidence that the first answer concealed. The larger claim is that human psychology offers a general, language-based strategy for debiasing LLMs that could remain useful as models change and specific biases shift.","feed_headline":"One phrase makes LLMs confess their own biases","feed_subtitle":"A single follow-up question surfaces implicit bias, unanswerable quiz items, and evidence LLMs initially omit.","key_machinery":"The central object is a metacognitive prompt, defined as a wording designed to make a decision maker reflect on the limits of their own thinking. The specific instance is 'could you be wrong?', appended after the model has produced an answer. The machinery is the model's own autoregressive generation: because output must exist before it can be evaluated, the first answer provides a concrete target, and the follow-up prompt invites the model to produce an adversarial crowd within itself—counterarguments, contradictory evidence, alternative interpretations, and admissions of bias. The mechanism the paper proposes is retrieval: LLMs trained on vast human text contain information about biases and counterevidence, but that information is only brought into the response when the prompt directs attention to it.","core_discovery":"On its own terms, the discovery is that an instruction-tuned LLM holds latent knowledge about the limitations of its own outputs, and that a single metacognitive follow-up question can make that knowledge explicit. Given the answer to a word-association prompt, the model acknowledges that its pairings are stereotypically biased; given a multiple-choice medical question about a fictional organ, it recognizes that the premise is fictitious and explains why the question cannot be answered; given a summary of the too-much-choice effect, it volunteers that meta-analyses found the effect size near zero. Because the model's autoregressive architecture can only evaluate content that has already been produced, the paper argues, making the initial output explicit and then inviting critique is what allows the latent knowledge to be used. The prompt thereby converts implicit bias into explicit output the user can see and act on, and it reveals cases where the model's interpretation of a user's question differs from the model's own later account of what it knew.","pith_inferences":["Editorial inference: the paper does not compare 'could you be wrong?' with generic elaboration prompts such as 'please explain further' or 'list caveats', so whether the metacognitive wording itself is causal, rather than any request for more text, remains untested.","Editorial inference: because the demonstrations record only the follow-up text, not a revised answer to the original question, a quantitative test of debiasing would compare initial and final answers and measure whether accuracy or fairness improves.","Editorial inference: the strategy's success presumably depends on the model's training data containing critique and counter-evidence; a model trained on filtered or narrower data would have less latent knowledge to surface.","Editorial inference: the same prompt could be embedded in a user-interface tool that automatically follows any high-stakes LLM answer with an adversarial check, but its effect on human decisions should be tested directly rather than assumed from the generated text."],"forward_implications":["Users can expose implicit bias in LLM word associations without specialized bias tests, by following any answer with the prompt 'could you be wrong?'.","A poor score on 'I don't know' multiple-choice questions does not prove an LLM lacks metacognitive knowledge; the capacity appears when self-critique is invited.","Factual summaries that omit failed replications or null meta-analytic results can be corrected on demand, giving users access to counter-evidence before relying on the summary.","Iterating the prompt can surface less prominent biases, such as omission bias in moral decisions, that the initial answer and even a first critique miss.","Because the intervention is a language prompt rather than a model-specific adjustment, the same strategy has a chance of transferring to future models."],"supporting_citations":[{"why":"Supplies the word-association test showing that explicitly unbiased LLMs still form biased associations, the first demonstration target.","marker":"11"},{"why":"Supplies the medical multiple-choice benchmark and the claim that LLMs lack metacognition, which the follow-up prompt challenges.","marker":"9"},{"why":"Supplies the meta-analysis finding a near-zero choice overload effect, the counter-evidence that first responses omit.","marker":"30"},{"why":"Supplies the human decision-making result that dialectical bootstrapping improves judgment, the template for the LLM prompt.","marker":"27"},{"why":"Supplies the omission-bias finding used to show that iterative self-critique reveals less prominent biases.","marker":"7"}],"fun_headline_variants":["Ask 'could you be wrong?' to reveal LLM bias","One follow-up question makes LLMs confess bias","LLMs expose hidden bias when asked to reconsider","One question cuts LLM bias: 'could you be wrong?'","Metacognitive nudge makes LLMs admit their limits"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The paper assumes that the self-critique triggered by the exact wording 'could you be wrong?' is doing the work, and that making a bias explicit counts as debiasing, rather than comparing against generic follow-up prompts or testing whether the original answer actually changes.","fun_headline_variants_meta":{"raw":{"variants":["Ask 'could you be wrong?' to reveal LLM bias","One follow-up question makes LLMs confess bias","LLMs expose hidden bias when asked to reconsider","One question cuts LLM bias: 'could you be wrong?'","Metacognitive nudge makes LLMs admit their limits"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000852,"raw_usage":{"total_tokens":3747,"prompt_tokens":1032,"completion_tokens":2715,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":648,"completion_tokens_details":{"reasoning_tokens":2635}},"tokens_in":648,"tokens_out":2715,"duration_ms":19442,"temperature":1.0,"reasoning_tokens":2635,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-06T17:38:07.809846+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run the same initial questions with generic follow-up prompts, such as 'please elaborate', 'any caveats?', or 'give me more detail', and score whether they elicit the same bias acknowledgements, unanswerability admissions, and counterevidence; if the generic prompts do, the metacognitive wording is not the causal ingredient. Also measure whether the model's answer to the original question changes after the follow-up, which the paper does not report.","supporting_citations":[{"cited_title":"& Griffiths, T","cited_arxiv_id":null,"evidence_quote":"Supplies the word-association test showing that explicitly unbiased LLMs still form biased associations, the first demonstration target."},{"cited_title":"& Yuksel, D","cited_arxiv_id":null,"evidence_quote":"Supplies the medical multiple-choice benchmark and the claim that LLMs lack metacognition, which the follow-up prompt challenges."},{"cited_title":"& Todd, P","cited_arxiv_id":null,"evidence_quote":"Supplies the meta-analysis finding a near-zero choice overload effect, the counter-evidence that first responses omit."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Supplies the human decision-making result that dialectical bootstrapping improves judgment, the template for the LLM prompt."},{"cited_title":"& Lieder, F","cited_arxiv_id":null,"evidence_quote":"Supplies the omission-bias finding used to show that iterative self-critique reveals less prominent biases."}],"review_version":1}