{"id":"bb11586b-d3d5-4ac6-9733-d51dd6c9c08f","arxiv_id":"2506.00064","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":4,"one_line_summary":"A new benchmark shows LLMs rarely detect and correct errors in user prompts unless explicitly instructed, and fine-tuning on error-handling examples greatly improves this ability.","lead":"This paper introduces Mis-prompt, a benchmark that tests whether large language models can spot and fix errors in a user's question without being told to. It finds that current models are largely unsuccessful at this, and that fine-tuning on such examples substantially boosts their performance.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"GPT-4o writes the gold answers and judges the responses, creating a circular evaluation; the human spot-check shares the same gold standard, so the decisive test is a gold-blind human rescoring of a random sample.","rationale":"The reader's weakest assumption is the GPT-4o gold+judge loop. I partially agree. The automated judge's bias toward GPT-4o style is partly mitigated by the human evaluation in Section 5.3, which shows a similar pattern and only 5.59% discrepancy; however, that human evaluation uses the same GPT-4o-generated gold answers as the scoring reference, so it does not test the correctness or exhaustiveness of the gold answers themselves. The remaining risk is that the benchmark's ground truth is not independently validated: only ~10% of the dataset is manually audited (Section 4.3.2), and the audit focuses on diversity and category assignment rather than factual correctness of every gold answer. The paper also does not yet release the dataset or code, so these checks cannot be run immediately. The proposed gold-blind rescoring would settle whether the low zero-shot F1 and the large SFT gains are artifacts of matching GPT-4o's gold style or reflect genuine proactive error-handling ability. Since the paper already carries a CONDITIONAL verdict and the concern does not warrant rejection, UNCHANGED is appropriate.","tokens_in":31148,"tokens_out":15550,"duration_ms":148096,"concrete_test":"Randomly sample 200 instances from the Mis-prompt test split (including both erroneous and correct prompts). Have three expert annotators, blind to GPT-4o gold answers, judge each evaluated model's response (e.g., GPT-4o, Claude-3.5, LLaMA-3.1-8B zero-shot and SFT) using the paper's four task criteria. Separately, fact-check the gold answers for these 200 prompts against Wikipedia/web sources. Recompute per-model F1 and the zero-shot-to-SFT gain using the gold-blind human labels. If the SFT gain and model ranking persist and fewer than 2% of gold answers contain factual errors, the self-reference concern is resolved; if the gain collapses or gold error rates are high, the reported numbers need revision.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central numbers in the paper—the zero-shot F1 averages in Table 3 and the SFT gains in Table 5—all depend on the validity of GPT-4o-generated gold answers (Section 4.2.1, Figure 12) and GPT-4o as the judgment model (Section 5.1.2, Figures 4–7). This is a circular setup: the judge determines 'correct' by entailment with a gold answer that GPT-4o itself wrote, and the SFT training data is also GPT-4o output. The paper's human evaluation (Section 5.3) checks only 10% of the test set and uses the same GPT-4o gold answers as the reference, so it does not independently validate the gold; the quality-control audit (Section 4.3.2) reviews about 10% of the dataset and finds 93.76% acceptable, leaving ~90% of gold answers unexamined. If even a small fraction of those gold answers are factually wrong, or if they encode a single correction style as the only acceptable one, then models offering alternative correct corrections will be marked wrong, zero-shot F1 will be underestimated, and SFT gains will partly reflect memorizing GPT-4o's style rather than genuine error-handling skill. Because both headline claims are quantified entirely through these F1 scores, this is the most load-bearing assumption in the paper.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper introduces Mis-prompt, a benchmark for proactive error handling in user prompts, defining four tasks (error detection, identification, correction, and guidance), a taxonomy of four primary and fourteen secondary error categories, and a dataset of roughly 15,000 erroneous prompts with GPT-4o-generated ground-truth responses. The authors evaluate 13 LLMs under zero-shot, 1-shot, 3-shot, CoT, and SFT settings, reporting that current LLMs achieve low zero-shot F1 scores (average 37.53%) and that SFT on error-handling instances substantially improves performance. The two headline claims are that current LLMs lack sufficient proactive error-handling capabilities and that SFT is an effective way to improve them.","tokens_in":31439,"tokens_out":5843,"duration_ms":55026,"significance":"If the evaluation were independently validated, Mis-prompt would fill a genuine gap: most existing error-handling benchmarks operate in a passive mode with explicit instructions, while Mis-prompt targets proactive handling of erroneous user prompts. The dataset, taxonomy, and the broad comparison across 13 models and five settings would be a useful community resource, and the authors are transparent about making the dataset publicly available. However, the current quantitative claims rest on an evaluation loop in which GPT-4o generates the gold answers, acts as the automatic judge, and also supplies the SFT training labels; the human evaluation does not break this loop because it uses the same gold answers as the reference. Consequently, the headline F1 numbers and the SFT gains are not yet supported as measures of genuine error-handling ability rather than stylistic mimicry.","major_comments":[{"comment":"The evaluation loop is self-referential. GPT-4o is used to generate the erroneous prompts and the gold answers (Section 4.2.1, Figure 12), and GPT-4o is also the automatic judge in Section 5.1.2, deciding whether a model response 'entails' the gold answer. The human evaluation in Section 5.3 uses the same gold answers as the reference, so it does not provide an independent check. The consequence is that the zero-shot F1 averages in Table 3 and the SFT gains in Table 5 may substantially reflect how closely a model's output matches GPT-4o's writing style rather than the actual quality of error handling. The authors should provide a gold-blind human rescoring of a random sample, in which human judges assess model responses without seeing the GPT-4o-generated gold answers, and report the agreement with the automated scores. This is needed to support the headline claim that current LLMs lack proactive error-handling capabilities.","section":"Sections 4.2.1, 5.1.2, 5.3"},{"comment":"The quality-control audit does not validate the gold answers. The 1,470 manually reviewed instances are checked for diversity, answerability, and category assignment, but the review protocol does not independently verify the factual or logical correctness of the ground-truth corrections and guidance. With only about 9.8% of the dataset audited, the correctness of the remaining roughly 13,500 gold answers is unestablished. If a non-negligible fraction of those gold answers are wrong or encode a single correction style as the only acceptable one, the automated F1 estimates in Tables 3 and 5 are biased. The authors should either release the audit details per instance or conduct an independent gold-answer validation on a larger sample.","section":"Section 4.3.2"},{"comment":"There is an internal inconsistency between Table 3 and Table 4 for the GPT-4o zero-shot results. Weighting the Table 4 primary-category F1 scores by the category sizes in Table 2 gives an average detection score of roughly 39.7%, not the 43.54% reported in Table 3; the category-weighted overall average from Table 4 is about 34.1%, not the 36.96% reported in Table 3. The authors should clarify whether Table 4 is computed on a different split, a different setting (e.g., CoT), or whether one of the tables contains an error. This matters because Section 5.2.2 uses Table 4 to support the claim that GPT-4o struggles with language errors and incomplete information.","section":"Tables 3 and 4"}],"minor_comments":[{"comment":"The dataset size is reported inconsistently: the Abstract and Section 1 say 14,696 instances, while Table 2 and Section 4.3.1 say 14,969. Please correct this.","section":"Abstract, Section 1, Table 2, Section 4.3.1"},{"comment":"Table 4 reports F1 scores in percentage, but the text refers to them as proportions (e.g., '0.5522' for Factual Errors). Please make the units consistent.","section":"Section 5.2.2"},{"comment":"The paper says an equal proportion of correct prompt data is sourced from TriviaQA, but it does not explain how the identification, correction, and guidance tasks are defined or scored for correct prompts. Please clarify the scoring protocol for these cases.","section":"Section 5.1.2"},{"comment":"The description of the human evaluation is incomplete: it states that Table 12 shows the human evaluation results and that the average discrepancy to the automated evaluation is 5.59%, but it does not describe how the human judges scored the responses or how the discrepancy was computed. Please provide the evaluation instructions and agreement details.","section":"Section 5.3"},{"comment":"There are typos: 'Appendice C.4' should be 'Appendix C.4', and 'Futhurmore' should be 'Furthermore'.","section":"Appendix C.4 and Limitations"}],"recommendation":"major_revision","confidential_remarks":"The circular evaluation is the main obstacle; a gold-blind human rescoring study would substantially strengthen the paper. The inconsistency between Tables 3 and 4 should be resolved before the quantitative claims can be fully trusted. The benchmark and dataset are potentially valuable, so I hope the authors can address these points in revision."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Adam, here's my take.\n\nThe paper earns its place: it names a real gap—error handling without explicit instructions—and builds a benchmark that operationalizes it as four tasks (detect, identify, correct, guide). The Table 1 contrast with prior error-handling benchmarks is accurate; the proactive angle is genuinely new. The SFT results are large and consistent across several open-weight models, and the category analysis (language vs factual vs logical) gives a useful breakdown.\n\nThe soft spot is the evaluation loop, and it's load-bearing. GPT-4o writes the gold answers, and GPT-4o then judges whether a model response 'entails' the gold answer. A valid correction that differs in wording or emphasis from the gold will be scored as incorrect. The human evaluation in Table 12 shares the same gold standard, so it doesn't break the circularity; it only checks whether humans agree with the judge on that gold. The quality audit reviews 10% of the dataset for internal quality, not for correctness of the gold answer. So the zero-shot F1 of 37.5% and the SFT jump to ~85% could partly reflect style-matching to GPT-4o rather than genuine error-handling skill. That is a real worry, not a nitpick.\n\nSmaller issues: the dataset isn't released (abstract says it will be), and there are minor inconsistencies—14,696 in the intro vs 14,969 in Table 2, and the Table 10 summary row repeats the Factual Errors row. These are distractors, not fatal.\n\nThe fix is straightforward and would make the benchmark solid: release the data, have humans rescore a blind sample against the original source facts rather than against the GPT-4o gold, and either accept multiple correct corrections or adjust the judge to allow semantic equivalence beyond entailment to the gold. If the headline numbers survive that check, the benchmark is useful.\n\nBottom line: I'd send this to peer review because the task formulation and first results are worth referee time, but I'd tell the authors the circularity needs to be addressed before acceptance. I wouldn't cite it myself until the data is out and the independent rescoring is done.","headline":"The proactive error-handling benchmark is a real contribution, but the GPT-4o-generated gold answers and GPT-4o judge make the headline F1 scores provisional until an independent blind rescoring is done.","tokens_in":32036,"tokens_out":1949,"would_cite":false,"duration_ms":19353,"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":"The paper introduces Mis-prompt, a benchmark testing whether LLMs can detect, identify, correct, and guide users about errors in prompts without being explicitly told to do so, and finds they largely cannot until fine-tuned.","keywords":["proactive error handling","LLM benchmark","error detection","error correction","error guidance","supervised fine-tuning","Mis-prompt","prompt errors"],"falsifier":"Re-score a random subset of model responses with a judge model that is not GPT-4o, or with human raters, and check whether the SFT gains persist; if they shrink to near chance, the benchmark's automated measure is style-sensitive.","tokens_in":30934,"feed_emoji":"⚠️","tokens_out":4434,"duration_ms":41685,"temperature":0.7,"pith_summary":"Large language models are usually tested for error handling only when the prompt explicitly asks them to check for mistakes. This paper argues that real users rarely give such instructions, and asks whether models can proactively catch errors on their own. To make that question measurable, it introduces Mis-prompt, a benchmark with four tasks — error detection, identification, correction, and guidance — built from a taxonomy of 14 error types and a dataset of about 15,000 erroneous prompts. Evaluating 13 models, the paper finds zero-shot performance is poor, with an average F1 of 37.53% and accurate correction at just 22.62%. It then shows that supervised fine-tuning on error-handling instances substantially improves these scores, for example raising Qwen-2.5-32B from 43.24 to 89.55 average F1.","feed_headline":"LLMs ignore errors in user prompts until explicitly told to check","feed_subtitle":"New Mis-prompt benchmark shows weak proactive handling and sharp gains from fine-tuning on error-handling examples.","key_machinery":"The Mis-prompt benchmark itself is the carrying mechanism. It operationalizes proactive error handling as four subtasks, each scored with binary labels: error detection (is there an error?), error identification (did the model attempt, and was it correct?), error correction (attempt and accuracy), and error guidance (does it help the user rephrase?). The dataset is built by converting FEVEROUS, CommonsenseQA, and ROCStories into erroneous prompts and by directly generating prompts across four primary and fourteen secondary error categories, with GPT-4o producing the ground-truth responses and a manual review pass checking quality. Evaluation uses GPT-4o as an automated judge that compares model responses to gold answers, with F1 as the metric.","core_discovery":"The central claim is that current LLMs lack sufficient proactive error-handling capabilities, particularly in error correction and guidance, and that SFT on error-handling instances is an effective remedy. The evidence is a benchmark where a model must decide whether a user's prompt contains an error, identify what is wrong, correct it, and guide the user to rephrase — without being told to do any of this. Across 13 models in the zero-shot setting, the average F1 is 37.53%, with accurate correction at 22.62% and guidance at 29.41%, while closed-source models such as Claude-3.5 reach 50.83%. After LoRA-based SFT on the Mis-prompt training split, the same open models jump to 81–89% average F1, and human evaluation on a 10% sample agrees with the automated scores within 5.59 percentage points.","pith_inferences":["Because the same GPT-4o model writes the gold answers and serves as the judge, part of the measured SFT improvement may reflect stylistic mimicry of the judge's preferred format; this could be tested by re-scoring with a different judge model.","The taxonomy's uneven results, such as near-chance performance on language errors, hint that future work could build specialized data or training for under-served error types.","One could extend the benchmark to measure whether proactive error handling actually changes the final answer the model gives, which would connect the capability to downstream correctness.","The findings suggest a testable hypothesis for real products: logging cases where users later correct the model's output could serve as a continual source of error-handling training data."],"forward_implications":["If proactive error handling is as weak as reported, models deployed in chatbots and assistants may silently propagate wrong assumptions in user queries.","SFT on error-handling instances offers a concrete recipe for improving this behavior, with LoRA fine-tuning sufficient for large gains.","The benchmark provides a reusable testbed for comparing future models on the four subtasks, making the capability measurable rather than anecdotal.","Few-shot prompting and chain-of-thought yield smaller gains than SFT, suggesting the skill is not easily elicited by instructions alone."],"supporting_citations":[{"why":"GPT-4o generates the ground-truth responses and serves as the automated judge, making it the load-bearing component of both data construction and evaluation.","marker":"(OpenAI, 2023)"},{"why":"FEVEROUS is one of the source datasets converted into erroneous prompts, anchoring the factual-error portion of Mis-prompt.","marker":"(Aly et al., 2021)"},{"why":"CommonsenseQA provides the source content for the logical-error portion of the converted dataset.","marker":"(Talmor et al., 2019b)"},{"why":"ROCStories is the source for incomplete-information error prompts derived from narrative text.","marker":"(Mostafazadeh et al., 2016)"},{"why":"Sentence-BERT computes cosine similarity for deduplication, shaping the final dataset's diversity.","marker":"(Reimers and Gurevych, 2019)"},{"why":"LoRA is the parameter-efficient fine-tuning method used for the SFT experiments that produce the main improvement claims.","marker":"(Hu et al., 2021)"},{"why":"The F1 metric follows this previous work and is the evaluation measure for all automated and manual score comparisons.","marker":"(Fatahi Bayat et al., 2023)"},{"why":"This work contributes the factual-error subcategories (relation, entity, circumstance) used in the taxonomy.","marker":"(Pagnoni et al., 2021)"},{"why":"This work supplies the logical-fallacy subcategories that structure the logical-error part of the taxonomy.","marker":"(Sourati et al., 2023)"}],"fun_headline_variants":["LLMs miss prompt errors without explicit instruction to check","Mis-prompt: LLMs fail proactive error handling","SFT lifts LLM proactive error handling from 37% to 80%+ F1","New benchmark: LLMs only fix errors when told to","Proactive error handling: LLMs stumble unless fine-tuned"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"That a GPT-4o-written gold answer plus a GPT-4o judge gives a valid measure of error-handling correctness; if the judge favors responses that resemble its own style, the reported gains from fine-tuning could be partly mimicry rather than genuine handling.","fun_headline_variants_meta":{"raw":{"variants":["LLMs miss prompt errors without explicit instruction to check","Mis-prompt: LLMs fail proactive error handling","SFT lifts LLM proactive error handling from 37% to 80%+ F1","New benchmark: LLMs only fix errors when told to","Proactive error handling: LLMs stumble unless fine-tuned"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.001231,"raw_usage":{"total_tokens":5013,"prompt_tokens":857,"completion_tokens":4156,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":473,"completion_tokens_details":{"reasoning_tokens":4068}},"tokens_in":473,"tokens_out":4156,"duration_ms":32299,"temperature":1.0,"reasoning_tokens":4068,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-07T12:44:59.599461+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Re-score a random subset of model responses with a judge model that is not GPT-4o, or with human raters, and check whether the SFT gains persist; if they shrink to near chance, the benchmark's automated measure is style-sensitive.","supporting_citations":[],"review_version":1}