Mitigating Hallucinations via Inter-Layer Consistency Aggregation in Large Vision-Language Models
Kai Tang, Jinhao You, Xiuqi Ge, Hanze Li, Yichen Guo, Xiande Huang
TL;DR
This paper introduces DCLA, a decoding method that reduces hallucinations in large vision-language models by enforcing inter-layer consistency without retraining, significantly improving output reliability.
Contribution
The paper presents a novel, training-free decoding mechanism that aggregates layer representations to mitigate hallucinations in LVLMs, enhancing robustness and performance.
Findings
DCLA effectively reduces hallucinations on benchmarks like MME and POPE.
The method improves the reliability and consistency of LVLM outputs.
DCLA requires no retraining or external knowledge, making it practical for deployment.
Abstract
Despite the impressive capabilities of Large Vision-Language Models (LVLMs), they remain susceptible to hallucinations-generating content that is inconsistent with the input image. Existing training-free hallucination mitigation methods often suffer from unstable performance and high sensitivity to hyperparameter settings, limiting their practicality and broader adoption. In this paper, we propose a novel decoding mechanism, Decoding with Inter-layer Consistency via Layer Aggregation (DCLA), which requires no retraining, fine-tuning, or access to external knowledge bases. Specifically, our approach constructs a dynamic semantic reference by aggregating representations from previous layers, and corrects semantically deviated layers to enforce inter-layer consistency. The method allows DCLA to robustly mitigate hallucinations across multiple LVLMs. Experiments on hallucination benchmarks…
Peer Reviews
Decision·ICLR 2026 Conference Withdrawn Submission
- Method is training-free, i.e. can be integrated into existing LVLMs without retraining. - The paper evaluates across multiple LVLMs and benchmarks, showing improvements. - The dynamic layer correction mechanism is interesting.
- The mechanism for adaptive correction (triggering threshold based on cosine similarity) is heuristic. No justification is provided for the choice of metric, threshold, or weighting function. In ablation, it's further observed that model is sensitive to the hyperparameter and table 6 shows very different number for different models. The optimal values for α = 0.82 and τ = 0.74 is too specific without any intuitive guidance. It seems impractical to tune hyperparameters for this method. - The exp
1. Well-Motivated Method: The method's design is highly motivated and clearly articulated, based on a compelling analysis of LVLM internal dynamics. The premise that: (1) "hallucinations often manifest as localized surges at the later layers, which tend to override earlier, visually grounded information," and (2) "Earlier layers encode basic semantic structures that remain robust to overfitting and noise introduced in deeper layers," provides a strong foundation for the proposed DCLA framework.
Major Concerns While the foundational idea of leveraging stable semantic references from early layers is novel, the implementation of the DCLA mechanism, specifically the layer aggregation method (especially the weight calculation) and the strategy for selecting which layers to correct, appears to be heavily based on heuristics, necessitating a very strong empirical validation (significant performance gains). That is to say, given the heuristic design, the practical efficacy is paramount. Howe
1. The experiments are extensive.
1. The performance gain on POPE (Table 3) is **marginal** across all settings (improvements within 1%). It’s unclear whether these gains exceed the standard deviation. 2. A major concern is that some claims are not well supported by experiments or prior studies, which makes the argument confusing. For example, in line 229, “However, this unidirectional information flow can lead to the gradual loss of fine-grained semantic cues captured by earlier layers.” This claim lacks empirical evidence or
* The approach is simple yet novel, presenting a training-free method for hallucination mitigation that avoids retraining or fine-tuning, making it both practical and widely applicable across different models and settings. * The paper is well written and clearly structured, with explanations that make the methodology and results easy to understand and follow.
* __Hyperparameter selection__: It is unclear how the hyperparameters for the proposed method were chosen. If they were optimized using the test set, this could compromise the validity of the results. Clarification on whether a separate training or validation set was used is needed. * __Missing benchmark evaluations__: The paper does not report results on established hallucination benchmarks such as CHAIR [1] or AMBER [2], which would provide a more comprehensive evaluation of the method’s effe
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Digital Media Forensic Detection · Advanced Image Processing Techniques