Title: On Epistemic Uncertainty of Visual Tokens for Object Hallucinations in Large Vision-Language Models
Venue: NeurIPS 2025
Accepted: yes
Keywords: Large Vision-Language Models

Object Hallucinations

Uncertain Visual Tokens
Abstract: Large vision-language models (LVLMs), which integrate a vision encoder (VE) with a large language model, have achieved remarkable success across various tasks. However, there are still crucial challenges in LVLMs such as object hallucination, generating descriptions of objects that are not in the input image. Here, we argue that uncertain visual tokens within the VE is a key factor that contributes to object hallucination. Our statistical analysis found that there are positive correlations between visual tokens with high epistemic uncertainty and the occurrence of hallucinations. Furthermore, we show theoretically and empirically that visual tokens in early VE layers that exhibit large representation deviations under small adversarial perturbations indicate high epistemic uncertainty. 
Based on these findings, we propose a simple yet effective strategy to mitigate object hallucination by modifying the VE only. Our method comprises a proxy method with adversarial perturbations for identifying uncertain visual tokens efficiently and a method to mask these uncertain visual tokens during the self-attention process in the middle layers of the VE, suppressing their influence on visual encoding and thus alleviating hallucinations. Extensive experiments show that our method significantly reduces object hallucinations in LVLMs and can synergistically work with other prior arts.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\n### Background\n- LVLMs suffer from object hallucination, that is, they generate descriptions of objects that are not in the input image.\n### Method\n- The authors argue that uncertain visual tokens within the vision encoder in LVLMs are a key factor.\n\nStatistical analysis shows that high epistemic uncertainty in visual tokens => the occurrence of hallucinations.\n\nFurthermore, they show theoretically and empirically that in early visual encoder layers, large representation deviations under small adversarial perturbations => high epistemic uncertainty.\n\n- Based on their findings, the authors propose a new method to alleviate object hallucination for LVLMs.\n\nSpecifically, the method uses adversarial perturbations to identify uncertain visual tokens.\n\nThen, it applies a binary uncertainty mask to attention outputs in visual encoders, reducing the influence of uncertain visual tokens and alleviating object hallucination.\n### Results\n- In experiments, the proposed method can be combined with different prior approaches, significantly reducing object hallucinations in various LVLMs.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n- The paper proposes a new perspective: uncertain visual tokens from visual encoder can result in object hallucination.\n- The experiments are comprehensive, indicating that the proposed method can improve different LVLMs and prior approaches.\n\n## Weaknesses\n### Major\n\n- A few papers apply adversarial perturbance to images for mitigating hallucination. I appreciate that the authors dig into vision encoders and also show their method is compatible with prior ones, but I am not sure if the proposed method is technically novel or provides new insights. This may be very subjective, so I would like to see opinions from ACs and other reviewers.\n- Recent works [1-2] show that LVLMs tend to believe language priors (blind faith in text) instead of visual information. I think, the proposed method only modifies visual information from vision encoders and cannot mitigate hallucination caused by language priors. For example, when an LVLM is describing a table, it may tend to say there is a chair, because the text training data usually contains a table and a chair together. \n\n[1] VLind-Bench: Measuring Language Priors in Large Vision-Language Models\n\n[2] Words or Vision: Do Vision-Language Models Have Blind Faith in Text?\n\n- The evaluated LVLMs are kind of old. There are many new LVLMs with improved visual information processing modules, like Cambrian-1, Qwen2.5-VL, BLIP-3o and so on. I'm not sure if the proposed method still works on them. Besides, now that new LVLMs are using better vision modules, the benefit from the proposed method may shrink.\n\n- Masking some tokens may lead to information loss. As shown in Figure 7, I notice the LVLMs answers are much shorter when using the proposed method. It seems that, after we delete uncertain tokens, the models tend to describe fewer objects. Does this lead to less informative answers from LVLMs? Will this hurt the performance of models on VQA or reasoning tasks?\n\n### Minor\n- In Section 3.1, the authors propose estimating uncertainty based on adversarial attack, and show that it aligns with MC dropout. But in 3.2.1, the authors start to use MC dropout (lines 175-178) to estimate uncertainty maps. I'm wondering if the proposed uncertainty estimation is not associated with object hallucination.\n\n- The proposed method is not specific to LVLMs but to vision encoders. The authors show that visual encoders may have uncertain tokens, misleading LVLMs to generate object hallucination. This issue may happen in other scenarios when we use foundation visual encoders. For example, if I use CLIP to do image classification (without LLM part), the classification results may also be mislead by the uncertain tokens in the encoder. Thus, I think the \"uncertain token\" issue may be a common problem in encoders, while object hallucination is just an application when we use visual encoders in LVLMs. The authors are recommended to explore other applications of their findings and method in the future.\n\nFor LVLMs, I expect that the authors can explore, how LVLMs handle uncertain information from the encoder and how LVLMs balance between visual information and language prior, which is missing in the paper.\n\n- Line 198, is the indentation a typo?\n\nQUESTIONS:\nPlease see the Weaknesses section.\n\nLIMITATIONS:\nYes. The limitations and potential negative societal impact are discussed in the supplementary materials.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-20"}

comments: SUMMARY:
This work identifies uncertain visual tokens in the vision encoder as a key cause of object hallucination in large vision-language models  and proposes a strategy to mitigate it, leading to significant improvements on some benchmark evaluations.

STRENGTHS AND WEAKNESSES:
Strength:

The proposed method demonstrates promising performance on some benchmarks.

Weaknesses:

(1) The overall theoretical part appears to lack sufficient rigor, which may affect the reliability of its conclusions. For example, in Lemma 3.1:

1. What exactly is meant by “small perturbations”?
2. What does “locally follows” refer to?
3. Additionally, the hidden states “z” and “$z^*$” are introduced without prior definition or explanation.

(2) More importantly, in the proof of Lemma 3.1 and Theorem 3.2, the use of the “$\approx$” symbol is concerning. Drawing theoretical conclusions based on an imprecise approximation is not appropriate in a scientific context and raises questions about the soundness of the argument.

(3) In Theorem 3.2, it is meaningless to state "$\text { upper bound of the differential entropy of } Z_i^{(t)} \text { increases as } \mathbb{E}_\epsilon\left[\left\|Z_i^{(t)}-z_i^{(t)}\right\|_2^2\right] \text { increases. }$" We can say that the upper bound of any value X increase as any other (non-negative) value Y increases, because, for example, we can simply set the upper bound of X to be X+Y

(4) There is no significant performance gap between the proposed method and baseline methods in POPE benchmark.

QUESTIONS:
see Strengths And Weaknesses.

LIMITATIONS:
N/A
TITLE: Official Review
CLARITY: 1
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-06-29

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper addresses a research question:\n-  LVLMs' object hallucination by epistemic uncertainty.\n\nFirst, the paper discovers the uncertainty of visual tokens in a visual encoder of LVLM. To approximate the uncertainty, the paper generates a perturbed image by adding small noise by using adversarial attacks. After that, the mask is extracted from the layer-wise uncertainty map. Compared to MC dropout, the approximated epistemic uncertainty map is aligned; the computation is faster. The paper investigates how the hallucination positively correlates with uncertainty. Based on the approximated uncertainty, the paper proposes a masking strategy, which is training-free. \n\nThe paper validates the proposed method on both open-generation and VQA-style benchmarks. When applying the proposed method to the prior work, the performances are generally improved. The ablation studies, such as vision encoder layers, and masking strategies, also validate the proposed method.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n- Both uncertainty and hallucinations are important research for trustworthiness. Connecting this research is interesting and promising.\n- The paper has proposed the approximation method for uncertainty, which is faster than the MC method. \n- The writing is well-structured as follows: Efficient uncertainty approximation -> Analysis of uncertainty and hallucination -> Mitigation using uncertainty map -> Validation of the proposed method.\n\n## Weaknesses\n- Although the proposed approximation is efficient, what is the inference time compared to other techniques, e.g., VCD without the proposed method?\n- As shown in Fig. 7 of the paper and the qualitative results of the appendix, the length of generation is shorter than the method without the proposed method. Thus, including the average length of the generation would be helpful in understanding the proposed method. Additionally, I think that a discussion is needed on this point.\n- In Table A10 of the appendix, using MC dropout is not better than the gradient-based method. As I understand the main paper, MC dropout is more accurate but inefficient. I wonder why the performance of adversarial attacks is better than MC dropout.\n- The discussion with the prior work [1] regarding the proposed uncertainty map would be beneficial.\n[1] Hanjing Wang and Qiang Ji, Epistemic Uncertainty Quantification For Pre-trained Neural Networks, CVPR 2024\n\nQUESTIONS:\nPlease see above weaknesses.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses object hallucination in vision-language models (LVLMs), where models generate descriptions containing non-existent objects. The authors link this issue to uncertainty in visual tokens from the vision encoder and propose an efficient solution using adversarial perturbations to identify and mask uncertain tokens. Their method reduces hallucinations by 38% in benchmarks like CHAIR while maintaining output quality, and works 5x faster than traditional uncertainty estimation techniques. The approach is compatible with existing LVLM architectures and requires no additional training.\n\nSTRENGTHS AND WEAKNESSES:\n----------- Strength -----------\n1.\tThe proposed inference-time masking strategy reduces object hallucinations without fine-tuning or additional data, preserving model capabilities while improving output reliability through selective suppression of uncertain visual tokens identified via adversarial perturbations.\n2.\tUnlike conventional approaches that frequently compromise output quality while addressing hallucinations, the proposed method achieves substantial reductions in spurious object generation while consistently maintaining—and in certain cases even enhancing—both the F1 metric performance and the overall descriptive accuracy of generated captions. This dual benefit of hallucination suppression and quality preservation stems from the targeted, uncertainty-aware masking mechanism that selectively filters only the most unreliable visual tokens without disrupting valid visual information flow.\n3.\tThe comprehensive evaluation framework encompasses multiple state-of-the-art LVLM architectures (LLaVA-1.5, Shikra, MiniGPT-4) and rigorously assesses performance across established benchmarks (CHAIR, POPE, AMBER), demonstrating consistent effectiveness in hallucination reduction while maintaining model performance across different architectural paradigms and evaluation metrics. This systematic validation confirms the method's architectural agnosticism and practical viability for real-world deployment scenarios.\n4.\tThe paper excels in clarity, presenting complex technical concepts through well-structured explanations and intuitive visualizations. It maintains a strong balance between mathematical rigor and accessibility, particularly in explaining how adversarial perturbations reveal epistemic uncertainty in visual tokens. The writing effectively guides readers through both theoretical foundations and practical implementations.\n\n----------- Weakness -----------\n1.\tWhile Theorem 3.2 shows a consistent relationship between feature changes caused by adversarial perturbations (‖Δz‖₂²) and the upper limit of visual token uncertainty — effectively identifying unstable, high-uncertainty tokens as key triggers for hallucinations — does this vision-focused approach truly capture all problematic visual tokens? Specifically, could the current method miss certain troublesome tokens because of: (1) language model biases (e.g., frequent object pairings) that don’t depend on visual uncertainty; (2) mismatches between visual and language information that are not caught by looking at visual changes alone; or (3) errors that happen later during decoding, beyond what visual-layer analysis can detect?\n2.\tGiven the observed reduction in efficacy when applied to Q-Former-based architectures like MiniGPT-4, how might the proposed uncertainty-aware masking strategy be adapted to effectively address the bottleneck created by intermediate query-based representation layers, which currently attenuate the impact of vision encoder modifications on final model outputs? Specifically, could jointly optimizing perturbations across both visual and query token spaces, or developing cross-modal uncertainty propagation mechanisms, help bridge this performance gap while maintaining the method's training-free advantages?\n3.\tWhile the proposed method effectively reduces object hallucinations as demonstrated on CHAIR and POPE benchmarks, does it simultaneously maintain robust performance on the comprehensive MME evaluation? Specifically, does the vision token masking approach preserve the model's essential visual perception and reasoning capabilities across MME's diverse tasks, particularly for fine-grained recognition and complex compositional understanding? This verification is crucial to ensure the hallucination mitigation does not compromise the model's fundamental multimodal generation quality.\n4.\tGiven the demonstrated efficacy of the proposed uncertainty-aware masking approach across several LVLM architectures, how might its performance generalize to state-of-the-art models like DeepSeek-VL, particularly considering potential differences in visual token representation distributions, cross-modal alignment mechanisms, and inherent uncertainty patterns that could influence both hallucination suppression effectiveness and core multimodal capabilities? This critical evaluation gap warrants investigation to fully establish the method's applicability across next-generation vision-language models.\n\nQUESTIONS:\nplease see my weakness part.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}