{"id":"e2f9e8d8-ddeb-43fe-b3bf-fc24ed83380a","arxiv_id":"2412.06676","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":1,"one_line_summary":"A self-supervised [IDK] training objective lets LLMs abstain from uncertain answers, improving factual precision while sacrificing some knowledge recall.","lead":"This paper adds a special [IDK] token to a language model and changes the training loss so that, when the model would answer wrongly, some of the blame is shifted onto that token. The result is higher factual precision on question-answering benchmarks, at the price of lost recall, especially for small models.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Claimed small recall loss is undermined by a regularizer that never penalizes [IDK]-as-top-prediction on known facts; PopQA recall dropping from 35.5 to 20.5 is the expected failure mode.","rationale":"The reader's CONDITIONAL verdict is appropriate, and I agree that the recall drop on PopQA contradicts the 'small loss' framing. However, the reader's weakest_assumption emphasizes transfer from arbitrary next-token errors to factual QA, whereas the more precise load-bearing issue is internal to the objective: Equation 4 only applies LFP-reg when lambda = 0, i.e., when the model's top token is already correct. Thus the regularizer never penalizes the exact case where the model abstains on a fact it knows. This is not an external assumption about distribution shift; it is a property of the loss that makes the observed recall loss mechanistically expected. The evidence for the method's precision gains is real and the +The Pile control is a good check, but the central claim that recall loss is small is overstated, and the paper needs to either revise that claim or add a regularizer that acts when [IDK] is the argmax. I do not see grounds to reject the method outright, because the precision gains are consistent across datasets and the flaw is directly addressable. The verdict should remain CONDITIONAL, with the condition being a corrected and honestly quantified recall tradeoff, plus error bars or multi-seed results to establish that the precision gains over baselines are not noise.","tokens_in":21961,"tokens_out":6655,"duration_ms":75570,"concrete_test":"Construct a subset of PopQA and LAMA instances where the base Mistral-7B model answers correctly. Run the IDK-tuned checkpoint on this subset and record instances where it outputs [IDK]. By the training objective, LFP-reg was never active on these false-positive abstentions, because lambda > 0 whenever [IDK] is the argmax. Then retrain a variant with an oracle regularizer that penalizes [IDK] whenever it is the argmax and the base model is correct, and re-measure recall on PopQA. If recall remains near 20.5, the missing penalty is not the cause; if recall recovers substantially, the paper's 'small recall loss' claim depends on an unpenalized and easily fixable failure mode.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's central claim in Section 4 is that IDK-tuning gives 'a large increase in factual precision ... while causing only a small decrease in recall.' The design of the anti-false-positive regularizer makes this claim insecure. In Equation 4, LFP-reg is applied only when lambda = 0, and by Equation 2 lambda = 0 only when the model's argmax prediction is already the gold token. Therefore the model is never directly penalized for making [IDK] its top prediction on an example it actually knows. Once [IDK] becomes the argmax, lambda > 0 and the loss in Equation 1 actively pushes additional probability mass onto [IDK], reinforcing the false abstention. The regularizer can only act before the model crosses the threshold into [IDK]-top territory, so it does not directly protect against the failure mode it is named for. The PopQA result is a concrete manifestation: recall drops from 35.5 to 20.5, a 42% relative loss of previously known facts, which is not accurately described as 'only a small decrease in recall.' Because the central contribution is a precision-recall tradeoff, this unpenalized failure mode is load-bearing: the claimed tradeoff is not established uniformly, and the mechanism intended to limit recall loss is partially ineffective for exactly the cases where recall is lost.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes IDK-tuning: a special [IDK] token is added to an LLM's vocabulary, and the standard next-token cross-entropy objective is modified during continued pretraining so that, when the model is uncertain, probability mass in the target distribution is shifted toward [IDK]. The shift is controlled by an uncertainty factor λ (Eq. 2) and an upper bound Π, with an additional false-positive regularization term LFP-reg (Eq. 3). The method is trained on unlabeled text from The Pile and evaluated on closed-book factual sentence completion (LAMA, TriviaQA, PopQA) and multiple-choice benchmarks, across Mistral-7B, Pythia 70m–2.8B, and BERT. The authors report large precision gains with, they claim, only small recall loss, and provide ablations of Π, adaptive vs. fixed λ, and LFP-reg.","tokens_in":22155,"tokens_out":4825,"duration_ms":52328,"significance":"If the main claim holds, IDK-tuning is a practically attractive self-supervised route to abstention-based hallucination reduction: it requires no labeled uncertainty data, is a drop-in modification of the language-modeling loss, and is evaluated across multiple architectures and sizes. The paper includes a useful control for continued training on The Pile, a scaling analysis across the Pythia suite, and detailed ablations, and the authors commit to releasing code and checkpoints. However, the central precision–recall tradeoff claim is not uniformly supported by the reported numbers, and the proposed anti-false-positive regularizer has a structural gap that limits its ability to prevent the very failure mode it targets. These issues are load-bearing because the value of the method is precisely the claimed precision/recall tradeoff.","major_comments":[{"comment":"The LFP-reg regularizer is only active when λ = 0, which by Eq. (2) means the gold token is the argmax prediction. Therefore the model is never directly penalized for making [IDK] its top prediction on an example it actually knows. Once [IDK] is the argmax, λ > 0 and the loss switches to LIDK, whose target assigns probability λ to [IDK]; depending on the current p([IDK]) relative to that λ, the gradient can even increase mass on [IDK] rather than reduce it. The regularizer can only act before the model crosses into the false-abstention regime, so it does not directly protect against the failure mode it is named for. The PopQA result in Table 1 (recall 35.5 → 20.5) is a concrete manifestation of this gap, and the mechanism intended to limit recall loss is therefore only partially effective for exactly the cases where recall is lost.","section":"Section 2.2, Eqs. (2)–(4)"},{"comment":"The abstract and Section 4 claim 'only a small decrease in recall' of factual knowledge, but Table 1 shows PopQA recall dropping from 35.5 to 20.5, a 42% relative loss, and LAMA Google-RE dropping from 48.1 to 40.6. These are not uniformly small decreases. The claim appears to be a summary over datasets that masks substantial task-specific degradation. The authors should either report the tradeoff per dataset and temper the global claim, or provide an analysis of why PopQA (which tests popular, often predictive facts) is disproportionately affected. Since the paper's principal contribution is the precision–recall tradeoff, this discrepancy is central rather than a presentation issue.","section":"Abstract and Section 4.1, Table 1"},{"comment":"All main results are reported from single training runs with no error bars, confidence intervals, or multiple seeds. Given that Section 4.3 shows that the same loss can produce divergent optimization behavior for smaller Pythia models, and given that the PopQA recall drop is large, single-run results are not sufficient to establish whether the observed tradeoff is systematic or partly a consequence of optimization instability. The paper should at minimum state this limitation in the main text and, ideally, provide a small number of repeated runs for the central configurations or a sensitivity analysis around the reported numbers.","section":"Sections 3.1 and 4.1"}],"minor_comments":[{"comment":"The notation in Eq. (2) uses prob(yt = [gold] | y<t, x) and max_i(prob(yt = i | y<t, x)); it would be clearer to define these as model probabilities p_gold and p_max before using them in the formula, especially because the denominator is the max over the vocabulary including [IDK].","section":"Section 2.2, Eq. (2)"},{"comment":"The Confidence Threshold baseline is tuned on a development set to find the best threshold, while IDK-tuning uses a fixed Π = 1/2 with no tuning. This is a favorable comparison for the baselines, not the proposed method, but the asymmetry should be stated explicitly when interpreting the results.","section":"Section 3.2, baselines"},{"comment":"The caption contains a grammatical error: 'for of our IDK-tuned bert-base-cased' should read 'for our IDK-tuned bert-base-cased'.","section":"Table 3"},{"comment":"'RougleL' is a typo for 'ROUGE-L'.","section":"Table 4"},{"comment":"The conclusion acknowledges that 'we potentially apply our objective for next-token predictions where it might be ill-posed' and suggests filtering for named entities as a fix. This is an important caveat that is not reflected in the abstract or the main-results discussion; it should be stated earlier, since the transfer of uncertainty signals from arbitrary text to factual QA is a core assumption of the method.","section":"Section 6, Conclusion"}],"recommendation":"major_revision","confidential_remarks":"The paper presents a promising idea and a substantial empirical study, but the central claim of a favorable precision–recall tradeoff is not established as stated: the PopQA recall loss is large, and the LFP-reg regularizer has a structural blind spot for false [IDK] predictions. I do not see a reason to reject the work outright, as the method could be made sound by revising the claims, reporting task-specific tradeoffs, and adding a regularizer that can act when [IDK] is the argmax. The lack of error bars is also relevant for a NeurIPS-level claim of 'small loss.'"},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Roi, read this one for a clean example of a good idea fighting an overstated conclusion. The IDK objective is genuinely new: a special token, a self-supervised loss that shifts target mass toward [IDK] when the model's own uncertainty is high (measured by the gap between gold probability and top probability). No labeled data, a proper control (Mistral on The Pile with standard CE), and a sensible scaling analysis across Pythia sizes. The ablations are informative and the writing is clear. The paper earns credit for the control and for releasing checkpoints (though the code link should be checked).\n\nThe soft spot is load-bearing. The abstract claims only a small loss of encoded knowledge, but PopQA recall drops from 35.5 to 20.5, a 42% relative loss. That is not small. Worse, the regularizer they designed to prevent false abstention, L_FP-reg, only fires when lambda=0, i.e., when the model's argmax is already the gold token. The moment the model flips to [IDK] on a known fact, lambda>0 and the loss pushes even more mass onto [IDK]. So the regularizer cannot directly penalize the exact failure mode it is named for. The empirical tradeoff curves show it helps reduce the IDK error rate, but it does not stop the self-reinforcing loop. This explains the PopQA result and should force a rewrite of the central recall claim.\n\nOther issues are more minor: no error bars or multiple seeds (they admit this in the checklist), and the exact training sample from The Pile is not specified, which hurts exact reproducibility. The confidence threshold baseline gets a threshold search on the dev set, which is fair but should be stated as such. The reader's worry about transfer from arbitrary next-token errors to factual QA is legitimate; the authors themselves note the objective might be ill-posed for some next-token predictions.\n\nNone of this makes the paper circular; the objective is trained on unlabeled data and evaluated on held-out benchmarks. The idea is plausible and likely useful for retrieval-augmented generation and hallucination mitigation. But the paper as written overstates what is shown. A serious referee should engage with it, but the authors need to temper the abstract, add seeds or bootstrap intervals, and ideally fix the regularizer so it penalizes false IDK even when IDK is the argmax. Who is this for? Anyone working on abstention, calibration, or selective prediction. I would bring it to a reading group; the stress-test point is a good teaching moment.","headline":"A genuinely new self-supervised abstention objective, but the 'small recall loss' claim is contradicted by PopQA and the anti-false-positive regularizer has a blind spot that explains it.","tokens_in":22764,"tokens_out":4110,"would_cite":false,"duration_ms":42889,"reading_group":"yes","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 claims that adding a special [IDK] token and shifting wrong-prediction probability mass toward it during continual pretraining lets language models abstain on answers they do not know, raising factual precision with only a small…","keywords":["hallucination mitigation","uncertainty estimation","[IDK] token","model calibration","selective prediction","continual pretraining","factual precision","knowledge recall"],"falsifier":"Run the IDK-tuned Mistral-7B on open-ended factual prompts that require multi-token answers, strip [IDK] out of the output stream, and measure the precision of the remaining text: if precision does not rise relative to the base model while the [IDK] firing rate stays high, the abstention signal is not tracking factual knowledge. A complementary check is to present the same factual question in easy and deliberately hard paraphrases and test whether the model abstains far more on the hard paraphrase even when the base model answers both correctly.","tokens_in":21680,"feed_emoji":"🤷","tokens_out":8528,"duration_ms":78715,"temperature":0.7,"pith_summary":"The paper tries to establish that a language model can be trained to abstain by emitting a special [IDK] token instead of a wrong answer, using only the model's own prediction errors on ordinary web text. The authors add the token to the vocabulary and modify the cross-entropy objective so that, whenever the model is wrong and uncertain, some of the probability mass meant for the correct token is redirected to [IDK]. Across several model families and factual benchmarks, IDK-tuned models answer far fewer questions incorrectly while losing only a small share of the facts the base model knew, and the gains hold against confidence-threshold, P(True), and semantic-entropy baselines. If the claim is right, it offers a self-supervised route to hallucination reduction that does not require labeled uncertainty data or answer choices.","feed_headline":"Adding an [IDK] token sharply boosts factual precision in LLMs.","feed_subtitle":"Self-supervised [IDK] training cuts wrong answers while keeping most factual knowledge intact.","key_machinery":"The load-bearing object is the [IDK] token plus the IDK objective, a replacement for the standard next-token cross-entropy loss. The loss is $L_{\\mathrm{IDK}} = L_{\\mathrm{CE}}(\\hat{y}, (1-\\lambda) y + \\lambda \\mathbf{1}_{[\\mathrm{IDK}]})$, where $y$ is the gold token's one-hot target and $\\mathbf{1}_{[\\mathrm{IDK}]}$ is a one-hot target for [IDK]. The uncertainty factor $\\lambda = \\Pi(1 - p(\\mathrm{gold}) / \\max_i p(i))$ decides how much target mass moves to [IDK]: zero when the gold token leads the prediction, approaching one when the model's best guess is far from the gold token. An anti-false-positive term is added only when the prediction is correct, so the model learns not to reach for [IDK] when it knows the answer.","core_discovery":"The central discovery, stated on the paper's own terms, is that an LLM's next-token prediction errors encode enough uncertainty signal to teach the model to know when it does not know. IDK-tuning adds a randomly initialized [IDK] token and modifies the standard next-token cross-entropy loss so that wrong predictions share some target probability mass with [IDK], with the amount determined by how far the gold token is from the model's top prediction. An additional regularization term suppresses [IDK] use on correct predictions. On LAMA, TriviaQA, PopQA, and the lm-eval-harness tasks, the resulting models show large precision gains with small recall losses, for instance raising Mistral-7B's precision on the LAMA Google-RE subset from 48.1 to 71.1 while recall falls from 48.1 to 40.6. The authors also show that the effect scales with model size and that IDK-tuning fails on the smallest 70M and 160M models, whose training diverges or collapses.","pith_inferences":["The paper evaluates only short completions; a reader might extend the same check to open-ended generation, where [IDK] would need to compete with natural-language refusals before the method becomes a deployable guardrail.","Because the loss fires on every wrong token in web text, the [IDK] token may partly encode 'hard-to-predict wording' rather than 'missing fact'; restricting the loss to entity-like tokens is a natural variant that could preserve more recall.","The collapse on 70M and 160M models hints at a minimum scale for self-taught abstention; probing that threshold would say which small deployed models can safely rely on this approach.","Since the hyperparameter $\\Pi$ controls the maximum mass shifted to [IDK], it acts as a precision-recall dial that applications could tune per domain without retraining."],"forward_implications":["On closed-book factual sentence completion, IDK-tuned Mistral-7B reaches higher precision than the base model on every reported benchmark, with F1 rising on LAMA, TriviaQA, and PopQA.","The precision gains come at a modest recall cost, and the authors report the same pattern on multiple-choice tasks from the evaluation harness.","The method transfers across architectures and sizes: BERT, Pythia models from 410M to 2.8B, and Mistral-7B all improve, with F1 growing roughly log-linearly with model scale.","Continual pretraining on The Pile with the standard loss does not reproduce the gains, so the authors attribute the improvement to the IDK objective rather than to new knowledge in the extra training text.","General language skills survive mostly intact: on three summarization benchmarks the IDK-tuned Mistral scores within a fraction of a ROUGE-L point of the base model."],"supporting_citations":[{"why":"Supplies the LAMA benchmark and the framing of language models as knowledge bases, which the paper uses as its primary factual evaluation.","marker":"[Petroni et al., 2019]"},{"why":"Defines Mistral-7B-v0.1, the main large model that is IDK-tuned and compared against baselines.","marker":"[Jiang et al., 2023]"},{"why":"Provides the Pythia model suite used to measure how IDK-tuning scales with model size.","marker":"[Biderman et al., 2023]"},{"why":"Supplies BERT and the masked-language-model objective used for one IDK-tuning configuration.","marker":"[Devlin et al., 2019]"},{"why":"Provides The Pile, the self-supervised corpus used for IDK-tuning and for the standard-loss control.","marker":"[Gao et al., 2020]"},{"why":"Defines the confidence-threshold selective-prediction baseline that IDK-tuning is compared against.","marker":"[Yoshikawa and Okazaki, 2023]"},{"why":"Supplies the P(True) self-evaluation baseline for uncertainty estimation.","marker":"[Kadavath et al., 2022]"},{"why":"Supplies the semantic-entropy baseline that clusters sampled generations to detect uncertainty.","marker":"[Kuhn et al., 2023]"},{"why":"Supplies the evaluation harness used for the ARC, HellaSwag, MMLU, TruthfulQA, WinoGrande, and GSM8k multiple-choice results.","marker":"[Gao et al., 2023]"}],"fun_headline_variants":["LLMs with an [IDK] token boost precision from 48% to 71%","Teach LLMs to say 'I don't know' - fewer hallucinations, small recall cost","IDK token: a simple fix that makes LLMs admit ignorance and stay factual","LLMs that know when they don't know: [IDK] token raises precision"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing premise is that uncertainty signals learned from ordinary next-token prediction errors on web text transfer to factual question answering, even though most of those errors concern grammar, style, and rare word patterns rather than missing knowledge.","fun_headline_variants_meta":{"raw":{"variants":["LLMs with an [IDK] token boost precision from 48% to 71%","Teach LLMs to say 'I don't know' - fewer hallucinations, small recall cost","IDK token: a simple fix that makes LLMs admit ignorance and stay factual","LLMs that know when they don't know: [IDK] token raises precision"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000951,"raw_usage":{"total_tokens":4046,"prompt_tokens":923,"completion_tokens":3123,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":539,"completion_tokens_details":{"reasoning_tokens":3039}},"tokens_in":539,"tokens_out":3123,"duration_ms":22787,"temperature":1.0,"reasoning_tokens":3039,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-11T19:25:13.504437+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run the IDK-tuned Mistral-7B on open-ended factual prompts that require multi-token answers, strip [IDK] out of the output stream, and measure the precision of the remaining text: if precision does not rise relative to the base model while the [IDK] firing rate stays high, the abstention signal is not tracking factual knowledge. A complementary check is to present the same factual question in easy and deliberately hard paraphrases and test whether the model abstains far more on the hard paraphrase even when the base model answers both correctly.","supporting_citations":[],"review_version":1}