Tracing and Mitigating Hallucinations in Multimodal LLMs via Dynamic Attention Localization
This addresses hallucinations in multimodal LLMs, which is a critical reliability issue for applications like image captioning and visual question answering, though it appears incremental as it builds on prior attention-based approaches.
The paper tackles the problem of hallucinations in multimodal LLMs where generated text conflicts with visual input, and introduces D-LEAF, a method that dynamically localizes and corrects errors during inference, achieving a 53% relative improvement on captioning benchmarks and approximately 4% gains in VQA accuracy and F1-score.
Multimodal Large Language Models (MLLMs) achieve strong performance on tasks like image captioning and visual question answering, but remain prone to hallucinations, where generated text conflicts with the visual input. Prior work links this partly to insufficient visual attention, but existing attention-based detectors and mitigation typically apply uniform adjustments across layers and heads, obscuring where errors originate. In this paper, we first show these methods fail to accurately localize problematic layers. Then, we introduce two diagnostics: Layer Image Attention Entropy (LIAE) which flags anomalous layers, and Image Attention Focus (IAF) which scores attention heads within those layers. Analysis shows that LIAE pinpoints faulty layers and IAF reliably ranks heads that warrant correction. Guided by these signals, we propose Dynamic Layer-wise Entropy and Attention Fusion (D-LEAF), a task-agnostic, attention-guided method that dynamically localizes and corrects errors during inference with negligible overhead. Furthermore, by establishing a connection between D-LEAF and DPO, we provide theoretical justification for the effectiveness of D-LEAF. Results show our D-LEAF delivers a 53\% relative improvement on standard captioning benchmarks, and on VQA both accuracy and F1-score improve by approximately 4\%, substantially suppressing hallucinations while preserving efficiency.