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REVIEW 4 major objections 4 minor 21 references

LLM Abstention Can Be a Prompt Artifact, in Addition to Genuine Uncertainty

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Adding an 'Unknown' option makes LLMs falsely abstain even on questions they can answer, and renaming the option to a random word like 'Cerulean' changes nothing, showing abstention is partly a prompt artifact rather than genuine…

desk verdict The random-word ablation is a real and memorable result, but the 'can answer when forced' claim rests on a coercive rerun prompt, and the paper's own algebra caps the known share near 28%. read the letter →

arxiv 2507.16199 v6 pith:LRYT26G6 submitted 2025-07-22 cs.CL

Zipeng Ling , Shuliang Liu , Yuehao Tang , Junqi Yang , Shenghong Fu , Chen Huang , Kejia Huang , Yao Wan
show 2 more authors
Zhichao Hou Xuming Hu
This is my paper · ORCID
classification cs.CL
keywords LLMabstentionInflationpromptartifactuncertaintycalibrationinstructiontuningTrue/Falsequestionslater-layeroverridebias
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 argues that LLM abstention is not only an expression of genuine uncertainty but also a prompt-induced artifact, a phenomenon it names Abstention Inflation. On True/False questions, adding an 'Unknown' option makes three frontier models abstain on 32.9% of items on average while accuracy drops by 15.75 percentage points; replacing the 'Unknown' label with a random word such as 'Cerulean' leaves the abstention rate unchanged. When the option is removed in a forced rerun, accuracy recovers to 52–75%, yet models attribute 95–100% of their abstentions to the item being objectively unknowable — an introspective gap in which the model denies capability it has. Representation probes locate the override in the later transformer layers, and factor analysis ties the bias to instruction tuning rather than stochastic noise. The stakes: any system or benchmark that consumes 'Unknown' outputs as calibrated uncertainty will inherit a format-dependent bias as though it were genuine doubt.

What carries the argument

The load-bearing device is the contrast between two versions of the same question: one with a designated extra option and one without. Ten settings build a ladder on this contrast: S1–S2 baseline vs. added 'Unknown'; S3 format conversion; S4 word-content ablation ('Unknown' → 'Indeterminate' → 'Cerulean'); S5 forced rerun without the option; S6 self-diagnosis; S7 reasoning-trace F1 plus a DeBERTa NLI probe; S8 a logit-lens read of $\log P(\text{``Unknown''})$ across all 33 layers of an open-weight model in base, instruction-tuned, and RL variants; S9 persistence across three redraws; S10 temperature, difficulty, size, and alignment sweeps. The key comparison is the TFQ-vs-MCQ double dissociation: the same extra option moves abstention by tens of points on binary logic questions and by only small margins on four-option MCQs, which is what separates a structural trigger from a semantic or difficulty effect.

What would settle it

Re-run S5 with a neutral instruction that only removes the 'Unknown' option ('Please choose True or False') and measure accuracy on the formerly abstained items; if it falls to the 50% chance level, the recovery was compliance with the pressure wording and the C2 capability claim collapses. A second decisive check is a direct mid-layer probe reading the gold True/False label from hidden states of an abstaining model: if the label is absent before the final layers, the C3 override story fails.

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Extended reading notes

Core claim

The central claim is that abstention behavior in LLMs is inflated by the structural presence of an extra response option, regardless of the option's meaning. The paper establishes four progressive propositions: (C1) the trigger is structural — 'Unknown' and 'Cerulean' behave identically, and almost nothing changes when the format is switched from True/False to letter-coded labels; (C2) the effect makes models deny knowledge they demonstrably have, since removing the option recovers 52–75% accuracy on the very items the model abstains on, while self-diagnosis denies any subjective incapability; (C3) the override happens at the output end of the network, with reasoning-trace quality essentially unchanged and the 'Unknown' logit rising only in the last layers; and (C4) the bias is stable across repeated draws and temperatures and is installed by instruction tuning, as base models show far lower abstention inflation than their instruction-tuned counterparts. The net position is that abstention is often a learned surface pattern, not a faithful uncertainty signal, and that benchmarks and routers should not take a single-format 'Unknown' label at face value.

Load-bearing premise

The S5 forced-rerun prompt urges the model to 'pay more attention', to overcome 'subjective ability limits', and states it 'must select one of the original labels', so the 52–75% recovery could be instruction-following rather than latent capability; the companion claim that mid-layer representations preserve the correct answer is inferred indirectly from a logit-lens contrast, not directly probed.

Editorial extensions

If this is right

  • Downstream systems that route on abstention — abstention-based routers, confidence routers, multi-agent pipelines — will inherit the extra-option bias as if it were genuine epistemic uncertainty.
  • Benchmark designers who include an 'Unknown' category should report Abs Rate beside accuracy and include a w/o-option rerun as a routine sanity check; otherwise the abstention number mixes inflated and genuine refusals.
  • Reformatting binary questions away from a True/False-with-extra-option setup removes most of the inflation at no capability cost, according to the format-ablation results.
  • Instruction tuning raises accuracy and false abstention together — the paper quantifies an 'abstention tax' near one point of extra abstention per point of accuracy gained in the vulnerable format.
  • Models still distinguish answerable from truly-unknown items by a wide margin, so the bias is a directional over-trigger rather than a collapse of the model's ability to tell the two populations apart.

Reading between the lines

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

  • This suggests the effect may be a general 'escape-hatch option' phenomenon: any extra, low-commitment option could act as an abstention slot, so a natural next test is whether 'None of the above' or a confidence scale triggers the same inflation on MCQ-style tasks — a comparison the paper does not run.
  • The C3 claim is indirect: mid-layer preservation is inferred from the absence of a mid-layer Unknown-logit rise, not from reading the gold label out of those layers. A direct hidden-state probe for True/False would distinguish 'override at the output' from 'the answer never formed'.
  • The abstention tax has a deployment corollary the authors leave implicit: if each point of instruction-tuning accuracy is bought with roughly a point of false abstention, then net delivered accuracy — answers users actually receive — may be roughly flat on TFQ-with-Unknown setups, making such channels costlier than headline Acc suggests.
  • Because the S5 rerun prompt tells the model to 'pay more attention' and that it 'must select one of the original labels', part of the 52–75% recovery could be compliance. Re-running S5 with a neutral instruction is the cleanest way to separate capability from instruction-following, which would directly test the paper's C2.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. This paper studies the effect of adding an "Unknown" option to True/False and multiple-choice prompts. Across three commercial LLMs and six benchmarks, it reports that on TFQs the extra option produces large abstention rates and accuracy drops (average −15.75% accuracy, 32.9% abstention), that replacing "Unknown" with a random word preserves the effect, that rerunning abstained items without the option recovers 52–75% accuracy, that reasoning traces remain largely unchanged while final answers switch to "Unknown", and that the bias persists across temperature and emerges with instruction tuning. The authors organize the work around four claims (C1–C4) tracing the phenomenon from prompt structure to representation and training origin.

Significance. If the effect is real, the paper makes a valuable methodological point: abstention rates are not directly interpretable as epistemic uncertainty, and benchmarks that include "Unknown" labels need counterfactual controls. The design has notable strengths: the S2 effect is large and consistent across models; the random-word condition (S4) is an elegant control; S9 provides persistence evidence; S10 shows temperature invariance; the trace evaluation includes manual verification of 100 samples; and Appendix E.3 offers falsifiable predictions. The paper is not circular: the key quantities are measured against external datasets and baseline prompts, not fitted to the conclusion. However, the strongest reading ("the model denies it can answer even when it can") hinges on a capability probe whose prompt is coercive and whose label balance is unreported, so the central C2 claim needs repair.

major comments (4)
  1. [§4.2.1 / Appendix I (S5)] The S5 rerun is not a neutral test of whether the model can answer the abstained items. The prompt replays the prior S2 response ending in "Unknown" and instructs the model to "pay more attention", "overcome subjective ability limits", and "must select one of the original labels: True or False". That is an explicit command to reverse the abstention, so the 52–75% recovery rate may measure instruction-following under pressure rather than latent capability. The obvious control is to run the plain S1 prompt (no "Unknown" option, no follow-up turn) on the abstention subset; the paper does not report this control. Since C2 and the abstract's "denies it can answer even when it can" rest on S5, this is load-bearing.
  2. [§4.2.1 / Appendix C.4] The "above 50% random baseline" interpretation of S5 is not verifiable without the label distribution of the abstention subset. If the subset is imbalanced toward True or False, always predicting the majority label gives accuracy above 50% without any latent knowledge; the paper never reports this balance. Moreover, the paper's own decomposition in Appendix C.4 (alpha = 2*P(correct|forced) − 1) yields a pooled known share of about 28%, meaning most abstentions remain consistent with genuine uncertainty. The text should report the label balance, provide per-label accuracy, and soften claims that S5 proves the model "can" answer.
  3. [§4.3.2 / Appendix D.1 (S8)] C3 overstates what S8 shows. The logit-lens experiment tracks log P("Unknown") across layers and demonstrates that the Unknown logit rises only in later layers, but it does not directly read out a True/False prediction from mid-layer hidden states; the authors concede this in Appendix D.1 ("does not directly read out a True/False prediction from mid-layer hidden states"). The "mid-layer representations preserve the correct answer" part of C3 is therefore inferred from the absence of a mid-layer Unknown rise plus the behavioral S5 result. A direct mid-layer probe of the gold label is needed before claiming representation-level preservation.
  4. [§4.2.2 / Appendix I (S6)] The self-diagnosis prompt is leading. It asks the model to choose between "subjective incapability" and "the question is objectively unanswerable - the given information is genuinely insufficient", which offers a face-saving justification for the model's prior "Unknown" answer. Unsurprisingly, 95–100% of responses select option B. This does not establish that the model "sincerely believes" the abstention is warranted; it may simply be a post-hoc rationalization consistent with its own previous output. The introspective-gap claim would be stronger with a less leading prompt or an open-ended attribution.
minor comments (4)
  1. [Table 1 / §4.1.3] The "FLD MCQ" and "FOLIO MCQ" columns are S3 conversions, but the header "Acc (S2)" may confuse readers into thinking these are separate S2 runs; clarify in the caption that these are S3 runs with MCQ-style letter labels.
  2. [§4.1.3 / Appendix I (S3)] The S3 prompt includes an additional instruction ("Select 'C. Unknown' ONLY if the relationship is genuinely undeterminable... Do NOT select it simply because you feel uncertain") that is absent from S2, so the S3-versus-S2 comparison is not perfectly controlled; the conclusion that question format is not the root cause should acknowledge this confound explicitly.
  3. [Abstract / §4.1.4] The abstract says replacing "Unknown" with a random word produces an "identical effect"; the data show statistically indistinguishable rates, not numerically identical rates. Use "statistically indistinguishable" or report confidence intervals for the difference.
  4. [General / §3.5] The paper reports many proportions without confidence intervals (e.g., 32.9% Abs Rate, 52.4% 3/3 persistence, 42.3% NLI-recoverable). Adding bootstrap confidence intervals and releasing the code and data would materially strengthen the quantitative claims.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the four claims rest on independent prompt manipulations and measurements, not on fitted parameters or self-citations.

full rationale

The paper's derivation chain is self-contained against external benchmarks and baseline prompts. C1 is established by comparing S1 (no extra option) with S2 (Unknown added) and by S3/S4 ablations that replace the option word, an independent manipulation rather than a restatement of the definition. C2 uses an S5 rerun on abstained samples plus an S6 self-report; even though the S5 follow-up is coercive ('You must select one of the original labels: True or False'), the 52-75% recovery is a measured external quantity and is not constructed from the claim it supports. C3 relies on paired trace F1, an NLI probe, and a logit-lens comparison; Appendix D.1 explicitly acknowledges that mid-layer preservation is inferred indirectly rather than probed directly, which is a validity limitation, not a circular reduction. C4 is tested with repeated draws, temperature sweeps, and Base-versus-IT model families. Appendix C.4's known-share formula alpha = 2P(forced)-1 is algebra applied to a measured accuracy, not a fitted parameter relabeled as a prediction, and Appendix C.7's tax ratio is a descriptive quotient of measured deltas. No load-bearing self-citation or imported uniqueness theorem appears in the argument. The noted limitations affect robustness and interpretation, but not circularity.

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

The central claims are empirical and do not depend on fitted parameters. The main assumptions are methodological: the forced rerun prompt is treated as a capability probe, the NLI classifier is treated as a valid judge of logical traces, and the logit-lens contrast is treated as localizing the decision. No new entities are postulated; 'Abstention Inflation' is a label for observed behavior.

assumptions (4)
  • domain assumption The S5 rerun prompt measures latent capability rather than compliance pressure.
    The prompt orders the model to 'pay more attention', 'overcome subjective ability limits', and 'must select one of the original labels'; the 52-75% recovery is read as proof of capability.
  • domain assumption The DeBERTa NLI probe on reasoning traces is a valid test of whether the trace reached the correct logical conclusion.
    Trace F1 is near 0.05 absolute, so the paper leans on NLI classifications and a 100-sample manual check to interpret traces.
  • domain assumption Logit-lens projections at the token before the answer reveal where the 'Unknown' decision is formed.
    The authors say mid-layer preservation is inferred indirectly, not directly probed (Appendix D.1).
  • ad hoc to paper The TFQ versus MCQ asymmetry is compatible with a purely structural extra-option trigger.
    The same fifth option in MCQ format changes Abs Rate by only 0-4.5%, so the structural claim needs option-set size or format as part of the mechanism.

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Cite this review

Pith. "Pith review of LLM Abstention Can Be a Prompt Artifact, in Addition to Genuine Uncertainty." pith.science (2026). https://pith.science/paper/LRYT26G6

@misc{pith2026250716199,
  author       = {Pith},
  title        = {Pith review of: LLM Abstention Can Be a Prompt Artifact, in Addition to Genuine Uncertainty},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LRYT26G6}},
  note         = {Machine review of arXiv:2507.16199}
}
read the original abstract

Large Language Models (LLMs) are increasingly trained to abstain from answering questions they are unsure about. However, this ability is often misused: in real-world applications, input prompts sometimes contain uncertainty elements, and driven by this, LLMs are inclined to abstain even on problems they are capable of solving. We argue that LLM abstention is not only an expression of genuine uncertainty; it is also an artifact that can be largely influenced by prompts. We name this phenomenon *Abstention Inflation*. We add "Unknown" as an extra option for LLMs to choose from; experiments show serious accuracy drops on True/False Questions (TFQs). Replacing "Unknown" with an unrelated random word produces an identical effect. We argue that LLMs are trained to imitate the surface pattern of *abstention*, rather than to express genuine uncertainty. Based on ten experiments, we support four claims that form a progressive argument: **(C1)** *Abstention Inflation* is triggered by the structural presence of an extra option, not by genuine uncertainty; **(C2)** further, it makes the model deny it can answer even when it can; **(C3)** at the representation level, this manifests as a later-layer output override; **(C4)** finally, this bias is stable and emerges through instruction tuning, rather than stochastic noise.

Figures

Figures reproduced from arXiv: 2507.16199 by the authors.

Figure 1
Figure 1. Background of problems: Adding an “Un￾known” Option results in serious LLM Abstention be￾havior, and adding a random word makes LLMs treat it as another abstention slot and choose it. Detailed ex￾planation and examples are in Section 4.1.4. this poses risks in several settings: (1) Question￾Answering (Ren et al., 2023): users may express uncertainty along with questions, and such phras￾ing can affect LLM behavior. (… view at source ↗
Figure 2
Figure 2. Representative experiments for the four claims about [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. S4 Word Content Ablation. Left: Replace [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Left: w/o “Unknown” Option Rerun, all ac [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 6
Figure 6. Figure 6: Logit-lens probe on both FLD and FOLIO. Left: the logit change ∆ log P(“Unknown”) across all 33 layers for prompt with and without “Unknown” option. Right: Comparison of log P(“Unknown”) be￾tween Abstention Inflation and Wrong predictions. Among all samples that LLMs d…
Figure 7
Figure 7. Figure 7: S9 Stability. Left: Persistence Across Re [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 8
Figure 8. Figure 8: S10 Factor Analysis. Left: FLD samples with [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]
Figure 9
Figure 9. Figure 9: S10 Factor Analysis. (A) Abs Rate by Model Size & Alignment. (B) Abs Rate heatmap across Model Size & Alignment. (C) Accuracy and Abs Rate scatter. task difficulty. As shown on the left of [PITH_FULL_IMAGE:figures/full_fig_p008_9.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

21 extracted references · 18 canonical work pages

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Reviewed August 6, 2026 · model on record in the stance chip above.