Mitigating Hallucinations via Inter-Layer Consistency Aggregation in Large Vision-Language Models
arXiv:2505.12343v2 Announce Type: replace Abstract: Despite the impressive capabilities of Large Vision-Language Models (LVLMs), they remain susceptible to hallucinations, where generated content is inconsistent with the input image. Existing training-free hallucination mitigation methods often suffer from unstable performance and high sensitivity to hyperparameter settings, which limits their practicality and broader adoption. In this paper, we propose Decoding with Inter-layer Consistency via Layer Aggregation (DCLA), a training-free decoding mechanism that requires no retraining, fine-tunin
The proliferation of Large Vision-Language Models (LVLMs) has brought immediate attention to their inherent hallucination problem, making mitigation a critical and timely research area.
Improving the reliability of LVLMs by addressing hallucinations is crucial for their adoption in high-stakes applications and for building trust in generative AI systems.
This research provides a practical, training-free method (DCLA) to reduce LVLM hallucinations, which could accelerate the deployment of more robust vision-language AI.
- · AI developers
- · Generative AI users
- · Robotics
- · Healthcare AI
- · Companies reliant on inaccurate LVLMs
- · AI models prone to hallucinations
More reliable LVLMs will enable their broader and safer integration into commercial and industrial applications.
Reduced hallucination rates might accelerate research into multi-modal AI agents by improving foundational model stability.
Increased trust in LVLMs could lead to new applications in sectors requiring high accuracy, potentially disrupting existing workflows that depend on human verification.
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Read at arXiv cs.LG