pith. machine review for the scientific record. sign in

arxiv: 2604.26419 · v1 · submitted 2026-04-29 · 💻 cs.CV · cs.AI

Recognition: unknown

Delineating Knowledge Boundaries for Honest Large Vision-Language Models

Junru Song , Yimeng Hu , Yijing Chen , Huining Li , Qian Li , Lizhen Cui , Yuntao Du
Authors on Pith no claims yet

Pith reviewed 2026-05-07 13:51 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords vision-language modelsknowledge boundariesrefusal capabilityfactual hallucinationsfine-tuningpreference optimizationVisual-Idk datasettruthful rate
0
0 comments X

The pith

Vision-language models can learn to recognize their own knowledge limits and refuse unknown questions after targeted fine-tuning.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to make large vision-language models more honest by teaching them when to say they do not know the answer to a visual question. It builds a model-specific dataset of unknown facts by repeatedly sampling the model and flagging questions where answers vary across runs. Supervised fine-tuning followed by preference optimization then aligns the model to refuse these unknowns rather than hallucinate. The approach raises the truthful response rate on the resulting test set from 57.9 percent to 67.3 percent. Internal representation checks indicate the model has learned genuine boundaries, and the gains hold in medical and perceptual domains outside the training distribution.

Core claim

By first creating a Visual-Idk dataset through multi-sample consistency probing to separate known from unknown visual facts, then applying supervised fine-tuning and preference-aware optimization such as DPO or ORPO, the framework enables VLMs to delineate their parametric knowledge boundaries, producing higher rates of truthful refusal on questions beyond their training data.

What carries the argument

The Visual-Idk dataset generated via multi-sample consistency probing, used as the basis for supervised fine-tuning followed by preference optimization to align refusal behavior.

If this is right

  • The truthful rate on unknown visual questions rises from 57.9 percent to 67.3 percent.
  • Internal probing confirms the model acquires genuine boundary awareness rather than surface-level refusal patterns.
  • The same pipeline produces gains on out-of-distribution medical and perceptual questions.
  • The resulting models behave as more prudent visual assistants by defaulting to refusal when evidence is absent.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The probing-plus-preference method could be tested on text-only language models to check whether the same consistency signal works without images.
  • In deployed systems the approach might lower the rate of high-stakes errors by making refusal the default for low-confidence inputs.
  • A practical next measurement would track whether users prefer the more cautious model in interactive visual question-answering tasks.

Load-bearing premise

Multi-sample consistency probing correctly identifies facts the model does not know, and this identification transfers cleanly into the fine-tuning process without adding new biases.

What would settle it

If after the full training pipeline the model still produces confident wrong answers on the same set of previously unknown questions at rates close to the baseline, or if internal activation probes no longer distinguish known from unknown inputs.

Figures

Figures reproduced from arXiv: 2604.26419 by Huining Li, Junru Song, Lizhen Cui, Qian Li, Yijing Chen, Yimeng Hu, Yuntao Du.

Figure 1
Figure 1. Figure 1: Left: The four knowledge quadrants of a VLM, adapted from [4]. Our goal is to transform Unknown Unknowns (hallucinations) into Known Unknowns (stan￾dard refusals). Right: Comparison of model behaviors on an unknown question. The model after alignment correctly identifies its internal knowledge gap and provides a truthful refusal (“I don’t know”), while the base model generates an incorrect response (“Morav… view at source ↗
Figure 2
Figure 2. Figure 2: The pipeline of Visual-Idk dataset construction. view at source ↗
Figure 3
Figure 3. Figure 3: Generalization on OOD scenarios. Numeric labels denote the Truthful rate (%). tuning is effective at establishing a conservative prior that prioritizes abstention when visual signals are severely degraded. (2) Preference-aware methods excel in specialized domain shifts. While SFT performs well under visual noise, CPO and ORPO demonstrate su￾perior calibration when encountering professional knowledge gaps. … view at source ↗
Figure 4
Figure 4. Figure 4: Examples of model responses before and after alignment. view at source ↗
read the original abstract

Large Vision-Language Models (VLMs) have achieved remarkable multimodal performance yet remain prone to factual hallucinations, particularly in long-tail or specialized domains. Moreover, current models exhibit a weak capacity to refuse queries that exceed their parametric knowledge. In this paper, we propose a systematic framework to enhance the refusal capability of VLMs when facing such unknown questions. We first curate a model-specific "Visual-Idk" (Visual-I don't know) dataset, leveraging multi-sample consistency probing to distinguish between known and unknown facts. We then align the model using supervised fine-tuning followed by preference-aware optimization (e.g., DPO, ORPO) to effectively delineate its knowledge boundaries. Results on the Visual-Idk dataset show our method improves the Truthful Rate from 57.9\% to 67.3\%. Additionally, internal probing also demonstrates that the model genuinely recognizes its boundaries instead of just memorizing refusal patterns. Our framework further generalizes to out-of-distribution medical and perceptual domains, providing a robust path toward more trustworthy and prudent visual assistants.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper introduces a framework to improve the refusal behavior of large vision-language models (VLMs) on queries outside their parametric knowledge. It first constructs a model-specific Visual-Idk dataset by applying multi-sample consistency probing to label facts as known or unknown for the target VLM, then performs supervised fine-tuning followed by preference optimization (DPO or ORPO) to align the model toward honest refusal. The central empirical claim is an increase in Truthful Rate from 57.9% to 67.3% on the Visual-Idk dataset, supported by internal probing evidence that the model recognizes its boundaries rather than simply memorizing refusal patterns, plus reported generalization to medical and perceptual out-of-distribution domains.

Significance. If the consistency-based labeling reliably isolates true knowledge gaps, the work offers a practical, model-specific route to more trustworthy VLMs by reducing hallucinations in long-tail domains. The combination of self-supervised dataset curation with preference optimization and the use of internal probing to distinguish genuine boundary awareness from pattern matching are constructive contributions. The reported generalization to medical/perceptual OOD settings would be valuable if substantiated, but the overall significance hinges on validation that the probing step does not introduce systematic labeling errors.

major comments (3)
  1. [Abstract] Abstract: The reported Truthful Rate improvement (57.9% to 67.3%) is presented without any information on Visual-Idk dataset size, number of queries, number of samples per query for consistency probing, baseline models, statistical significance testing, or controls for output variance due to temperature/decoding randomness. These omissions make it impossible to evaluate whether the lift reflects improved honesty or artifacts of the labeling procedure.
  2. [Dataset curation] Dataset curation description: The central assumption that multi-sample consistency probing separates known from unknown facts is load-bearing for both the Truthful Rate result and the internal-probing claim. No controls are described that isolate parametric knowledge from stochastic generation effects (e.g., repeated sampling at fixed temperature, prompt/image perturbations, or comparison against held-out ground-truth knowledge), raising the risk that a non-negligible fraction of 'unknown' labels are false positives and that subsequent SFT+DPO merely teaches refusal on inconsistently generated items rather than true boundaries.
  3. [Generalization experiments] Generalization section: The claim that the framework generalizes to medical and perceptual OOD domains inherits the same labeling reliability issue. Without quantitative results (e.g., Truthful Rate deltas, dataset sizes, or probing accuracy on those domains) or ablation showing that the consistency labels transfer without new biases, the generalization statement cannot be assessed.
minor comments (2)
  1. [Abstract] The abstract would be clearer if it named the specific VLM(s) used for the main experiments and the internal probing.
  2. [Abstract] Notation for 'Truthful Rate' should be defined on first use, including how refusal versus incorrect answers are scored.

Simulated Author's Rebuttal

3 responses · 1 unresolved

We sincerely thank the referee for the detailed and constructive feedback on our manuscript. We address each major comment point by point below, clarifying our approach where possible and outlining specific revisions to strengthen the presentation and validation of our claims.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The reported Truthful Rate improvement (57.9% to 67.3%) is presented without any information on Visual-Idk dataset size, number of queries, number of samples per query for consistency probing, baseline models, statistical significance testing, or controls for output variance due to temperature/decoding randomness. These omissions make it impossible to evaluate whether the lift reflects improved honesty or artifacts of the labeling procedure.

    Authors: We agree that the abstract should include these contextual details to allow proper evaluation of the results. In the revised manuscript, we will expand the abstract to report the Visual-Idk dataset size, the number of queries and samples per query used in consistency probing, the baseline models, and note that results are averaged across multiple decoding runs with fixed temperature to control for variance. We will also reference statistical significance testing (e.g., paired t-tests or confidence intervals) performed in the main experiments. revision: yes

  2. Referee: [Dataset curation] Dataset curation description: The central assumption that multi-sample consistency probing separates known from unknown facts is load-bearing for both the Truthful Rate result and the internal-probing claim. No controls are described that isolate parametric knowledge from stochastic generation effects (e.g., repeated sampling at fixed temperature, prompt/image perturbations, or comparison against held-out ground-truth knowledge), raising the risk that a non-negligible fraction of 'unknown' labels are false positives and that subsequent SFT+DPO merely teaches refusal on inconsistently generated items rather than true boundaries.

    Authors: We acknowledge that additional controls would further substantiate the probing method. While the current manuscript relies on multi-sample consistency at fixed temperature as a practical proxy for knowledge boundaries (supported by the internal activation probing results), we will add an ablation study in the revision. This will include results with varied sampling temperatures, prompt and image perturbations, and analysis of label stability. These additions will help isolate parametric knowledge from generation stochasticity. revision: partial

  3. Referee: [Generalization experiments] Generalization section: The claim that the framework generalizes to medical and perceptual OOD domains inherits the same labeling reliability issue. Without quantitative results (e.g., Truthful Rate deltas, dataset sizes, or probing accuracy on those domains) or ablation showing that the consistency labels transfer without new biases, the generalization statement cannot be assessed.

    Authors: We agree that the generalization claims require more quantitative detail. In the revised version, we will report specific Truthful Rate improvements, dataset sizes, and probing parameters for the medical and perceptual OOD domains. We will also include an ablation comparing consistency-based labels to available expert annotations in the medical domain to assess bias transfer. revision: yes

standing simulated objections not resolved
  • Direct comparison of consistency probing labels against comprehensive held-out ground-truth knowledge for the full Visual-Idk dataset, as such exhaustive ground-truth labels are not feasible to obtain for model-specific long-tail visual facts.

Circularity Check

0 steps flagged

No circularity: empirical pipeline with held-out evaluation

full rationale

The paper describes an empirical workflow: multi-sample consistency probing to label a Visual-Idk dataset, followed by SFT + DPO/ORPO alignment, with performance measured as Truthful Rate lift (57.9% → 67.3%) on that dataset. No equations, derivations, or self-referential definitions appear in the provided text. The central result is an external measurement on held-out data rather than a quantity forced by construction from the labeling procedure itself. No self-citation chains or uniqueness theorems are invoked to justify the method. The framework is therefore self-contained against its own benchmarks and receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The framework rests on the unstated premise that consistency across multiple samples is a valid proxy for parametric knowledge and that preference optimization will not degrade performance on known facts.

axioms (1)
  • domain assumption Multi-sample answer inconsistency indicates absence of parametric knowledge rather than stochastic generation noise.
    Invoked when curating the Visual-Idk dataset from probing results.
invented entities (1)
  • Visual-Idk dataset no independent evidence
    purpose: Model-specific collection of unknown visual facts for refusal training.
    Newly curated resource whose construction depends on the probing method.

pith-pipeline@v0.9.0 · 5494 in / 1300 out tokens · 37423 ms · 2026-05-07T13:51:10.429724+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

29 extracted references · 5 canonical work pages · 3 internal anchors

  1. [1]

    Advances in Neural Information Processing Systems37, 49706–49748 (2024)

    Brahman, F., Kumar, S., Balachandran, V., Dasigi, P., Pyatkin, V., Ravichander, A., Wiegreffe, S., Dziri, N., Chandu, K., Hessel, J., et al.: The art of saying no: Contextual noncompliance in language models. Advances in Neural Information Processing Systems37, 49706–49748 (2024)

    2024

  2. [2]

    Chen, Y., Hu, H., Luan, Y., Sun, H., Changpinyo, S., Ritter, A., Chang, M.W.: Can pre-trained vision and language models answer visual information-seeking ques- tions? ArXivabs/2302.11713(2023)

    work page arXiv 2023

  3. [3]

    Chen, Y., Hu, H., Luan, Y., Sun, H., Changpinyo, S., Ritter, A., Chang, M.W.: Can pre-trained vision and language models answer visual information-seeking ques- tions? In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. pp. 14948–14968 (2023)

    2023

  4. [4]

    ICML’24, JMLR.org (2024)

    Cheng, Q., Sun, T., Liu, X., Zhang, W., Yin, Z., Li, S., Li, L., He, Z., Chen, K., Qiu, X.: Can ai assistants know what they don’t know? In: Proceedings of the 41st International Conference on Machine Learning. ICML’24, JMLR.org (2024)

    2024

  5. [5]

    Advances in neural information processing systems36, 49250–49267 (2023)

    Dai, W., Li, J., Li, D., Tiong, A., Zhao, J., Wang, W., Li, B., Fung, P.N., Hoi, S.: Instructblip: Towards general-purpose vision-language models with instruction tuning. Advances in neural information processing systems36, 49250–49267 (2023)

    2023

  6. [6]

    In: Proceedings of the IEEE conference on computer vision and pattern recognition

    Goyal, Y., Khot, T., Summers-Stay, D., Batra, D., Parikh, D.: Making the v in vqa matter: Elevating the role of image understanding in visual question answering. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 6904–6913 (2017)

    2017

  7. [7]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    Gurari, D., Li, Q., Stangl, A., Guo, A., Lin, C.: Vizwiz grand challenge: Answering visual questions from blind people. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 3608–3617 (2018)

    2018

  8. [8]

    OpenAI Cookbook (2023),https : / / cookbook.openai.com/examples/using_logprobs

    Hills, J., Anadkat, S.: Using logprobs. OpenAI Cookbook (2023),https : / / cookbook.openai.com/examples/using_logprobs

    2023

  9. [9]

    In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

    Hong, J., Lee, N., Thorne, J.: Orpo: Monolithic preference optimization without reference model. In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. pp. 11170–11189 (2024)

    2024

  10. [10]

    In: International Conference on Learning Representations (ICLR) (2022)

    Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: LoRA: Low-rank adaptation of large language models. In: International Conference on Learning Representations (ICLR) (2022)

    2022

  11. [11]

    ACM Transactions on Information Systems43, 1 – 55 (2023)

    Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., Liu, T.: A survey on hallucination in large language mod- els: Principles, taxonomy, challenges, and open questions. ACM Transactions on Information Systems43, 1 – 55 (2023)

    2023

  12. [12]

    Li, Y., Du, Y., Zhou, K., Wang, J., Zhao, W.X., rong Wen, J.: Evaluating object hallucinationinlargevision-languagemodels.In:ConferenceonEmpiricalMethods in Natural Language Processing (2023)

    2023

  13. [13]

    In: Annual Meeting of the Association for Computational Linguistics (2021)

    Lin, S.C., Hilton, J., Evans, O.: Truthfulqa: Measuring how models mimic human falsehoods. In: Annual Meeting of the Association for Computational Linguistics (2021)

    2021

  14. [14]

    Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning

    Liu, F., Lin, K., Li, L., Wang, J., Yacoob, Y., Wang, L.: Mitigating hallucina- tion in large multi-modal models via robust instruction tuning. arXiv preprint arXiv:2306.14565 (2023)

    work page internal anchor Pith review arXiv 2023

  15. [15]

    Visual Instruction Tuning

    Liu, H., Li, C., Wu, Q., Lee, Y.J.: Visual instruction tuning. ArXiv abs/2304.08485(2023) 15

    work page internal anchor Pith review arXiv 2023

  16. [16]

    Advances in neural information processing systems36, 34892–34916 (2023)

    Liu, H., Li, C., Wu, Q., Lee, Y.J.: Visual instruction tuning. Advances in neural information processing systems36, 34892–34916 (2023)

    2023

  17. [17]

    In: Advances in Neural Information Processing Systems

    Lu, P., Mishra, S., Xia, T., Qiu, L., Chang, K.W., Zhu, S.C., Tafjord, O., Clark, P., Kalyan, A.: Learn to explain: Multimodal reasoning via thought chains for science question answering. In: Advances in Neural Information Processing Systems. pp. 2507–2521 (2022)

    2022

  18. [18]

    In: Proceedings of the IEEE/cvf conference on computer vision and pattern recognition

    Marino,K.,Rastegari,M.,Farhadi,A.,Mottaghi,R.:Ok-vqa:Avisualquestionan- swering benchmark requiring external knowledge. In: Proceedings of the IEEE/cvf conference on computer vision and pattern recognition. pp. 3195–3204 (2019)

    2019

  19. [19]

    In: European Conference on Computer Vision

    Ouali, Y., Bulat, A., Martinez, B., Tzimiropoulos, G.: Clip-dpo: Vision-language models as a source of preference for fixing hallucinations in lvlms. In: European Conference on Computer Vision. pp. 395–413. Springer (2024)

    2024

  20. [20]

    Advances in neural information processing systems36, 53728–53741 (2023)

    Rafailov, R., Sharma, A., Mitchell, E., Manning, C.D., Ermon, S., Finn, C.: Direct preference optimization: Your language model is secretly a reward model. Advances in neural information processing systems36, 53728–53741 (2023)

    2023

  21. [21]

    Frontiers of optoelectronics16(1), 1 (2023)

    Tan, M., Xu, J., Liu, S., Feng, J., Zhang, H., Yao, C., Chen, S., Guo, H., Han, G., Wen, Z., et al.: Co-packaged optics (cpo): status, challenges, and solutions. Frontiers of optoelectronics16(1), 1 (2023)

    2023

  22. [22]

    Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

    Wang, P., Bai, S., Tan, S., Wang, S., Fan, Z., Bai, J., Chen, K., Liu, X., Wang, J., Ge, W., et al.: Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution. arXiv preprint arXiv:2409.12191 (2024)

    work page internal anchor Pith review arXiv 2024

  23. [23]

    arXiv preprint arXiv:2412.11196 (2024)

    Wang, Y., Zhu, Z., Liu, H., Liao, Y., Liu, H., Wang, Y., Wang, Y.: Drawing the line: Enhancing trustworthiness of MLLMs through the power of refusal. arXiv preprint arXiv:2412.11196 (2024)

    work page arXiv 2024

  24. [24]

    In: Findings of the Association for Computational Linguistics: EMNLP 2024

    Xie, Y., Li, G., Xu, X., Kan, M.Y.: V-dpo: Mitigating hallucination in large vision language models via vision-guided direct preference optimization. In: Findings of the Association for Computational Linguistics: EMNLP 2024. pp. 13258–13273 (2024)

    2024

  25. [25]

    Science China Information Sciences67(12), 220105 (2024)

    Yin, S., Fu, C., Zhao, S., Xu, T., Wang, H., Sui, D., Shen, Y., Li, K., Sun, X., Chen, E.: Woodpecker: Hallucination correction for multimodal large language models. Science China Information Sciences67(12), 220105 (2024)

    2024

  26. [26]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Yu, T., Yao, Y., Zhang, H., He, T., Han, Y., Cui, G., Hu, J., Liu, Z., Zheng, H.T., Sun, M., et al.: Rlhf-v: Towards trustworthy mllms via behavior alignment from fine-grained correctional human feedback. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 13807–13816 (2024)

    2024

  27. [27]

    In: Proceed- ings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

    Zhang, H., Diao, S., Lin, Y., Fung, Y., Lian, Q., Wang, X., Chen, Y., Ji, H., Zhang, T.: R-tuning: Instructing large language models to say ‘i don’t know’. In: Proceed- ings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). pp. 7113–7139 (2024)

    2024

  28. [28]

    In: International Conference on Machine Learning

    Zhang, T., Liu, F., Wong, J., Abbeel, P., Gonzalez, J.E.: The wisdom of hindsight makes language models better instruction followers. In: International Conference on Machine Learning. pp. 41414–41428. PMLR (2023)

    2023

  29. [29]

    In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

    Zhang, X., Wu, C., Zhao, Z., Lin, W., Zhang, Y., Wang, Y., Xie, W.: Pmc-vqa: Visual instruction tuning for medical visual question answering. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). pp. 2507–2517 (2023) 16

    2023