Hallucination-aware Intermediate Representation Edit in Large Vision-Language Models
Summary: arXiv:2603.29405v1 Announce Type: cross
Abstract
Large Vision-Language Models have demonstrated exceptional performance in multimodal reasoning and complex scene understanding. However, these models still face significant hallucination issues, where outputs contradict visual facts. Recent research on hallucination mitigation has focused on retraining methods and Contrastive Decoding (CD) methods. While both methods perform well, retraining methods require substantial training resources, and CD methods introduce dual inference overhead. These factors hinder their practical applicability.
Introduction
The integration of vision and language processing in AI has led to remarkable advancements, particularly in the realm of large Vision-Language Models (VLMs). These models excel in tasks that necessitate understanding and reasoning across visual and textual modalities. Nonetheless, one of the most pressing challenges remains the occurrence of hallucinations—instances where the model generates outputs that do not align with the actual visual input. This phenomenon can undermine the reliability of these models, especially in critical applications.
Current Approaches to Hallucination Mitigation
Two primary strategies have emerged in the effort to mitigate hallucinations in VLMs:
- Retraining Methods: These involve retraining the models on curated datasets to enhance their accuracy and alignment with visual inputs. However, this approach demands extensive computational resources and time, making it less feasible for real-time applications.
- Contrastive Decoding (CD) Methods: These methods aim to refine the output by contrasting multiple interpretations of the input. While they have shown promise in reducing hallucinations, they introduce dual inference overhead, complicating the inference process.
Proposed Framework
To overcome the limitations of existing approaches, we introduce a novel framework designed for dynamically detecting hallucination representations and performing hallucination-eliminating edits on these representations. Our method operates with minimal additional computational cost, providing a more practical solution for real-world applications.
Key Features of Our Approach
- Dynamic Detection: The framework can identify hallucinated outputs in real-time, allowing for immediate intervention.
- Efficient Edits: By focusing solely on the hallucinated components, the system can make targeted edits that effectively eliminate inaccuracies without overhauling the entire output.
- State-of-the-Art Performance: Our extensive experiments reveal that this approach achieves state-of-the-art results on existing benchmarks, surpassing previous methods in both efficiency and accuracy.
- Robust Control: The framework provides powerful controllability over hallucinations, allowing users to manage and mitigate inaccuracies according to their needs.
Conclusion
The issue of hallucinations in large Vision-Language Models poses a significant challenge to their reliability and usability. Our proposed framework offers a promising solution, balancing efficiency with performance. By enabling dynamic detection and targeted edits of hallucination representations, we pave the way for more robust and trustworthy AI systems in multimodal reasoning. For those interested in exploring our work further, the code is accessible at GitHub.