When RAG Hurts: Diagnosing and Mitigating Attention Distraction in Retrieval-Augmented LVLMs
arXiv:2602.00344v2 Announce Type: replace-cross Abstract: While Retrieval-Augmented Generation (RAG) is one of the dominant paradigms for enhancing Large Vision-Language Models (LVLMs) on knowledge-based VQA tasks, recent work attributes RAG failures to insufficient attention towards the retrieved context, proposing to reduce the attention allocated to image tokens. In this work, we identify a distinct failure mode that previous study overlooked: Attention Distraction (AD). When the retrieved context is sufficient (highly relevant or including the correct answer), the retrieved text suppresses
This research addresses a critical limitation identified in Retrieval-Augmented Generation (RAG) for Large Vision-Language Models (LVLMs), building on prior work by proposing a new failure mode and mitigation, reflecting ongoing efforts to improve AI reliability.
Understanding and mitigating 'Attention Distraction' in RAG-enhanced LVLMs is crucial for developing more robust and trustworthy AI systems, directly impacting their performance on complex knowledge-based tasks and real-world applicability.
The identification of 'Attention Distraction' shifts the focus from merely insufficient attention to retrieved context to also addressing how highly relevant context can paradoxically hinder performance, requiring new mitigation strategies.
- · AI researchers
- · Developers of RAG-based systems
- · Users of advanced AI for VQA
- · AI systems prone to attention distraction
- · Developers relying on outmoded RAG mitigation strategies
Improved accuracy and reliability of RAG-enhanced LVLMs in knowledge-intensive visual question answering tasks.
Reduced incidence of AI 'hallucinations' or incorrect inferences stemming from misinterpretations of high-quality retrieved data.
Accelerated development of more sophisticated multi-modal AI agents capable of nuanced information processing and reasoning.
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Read at arXiv cs.CL