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MAP: Mitigating Hallucinations in Large Vision-Language Models with Map-Level Attention Processing
arXiv – CS AI|Chenxi Li, Yichen Guo, Benfang Qian, Jinhao You, Kai Tang, Yaosong Du, Zonghao Zhang, Xiande Huang|
🤖AI Summary
Researchers developed MAP (Map-Level Attention Processing), a training-free method to reduce hallucinations in Large Vision-Language Models by treating hidden states as 2D semantic maps. The approach uses attention-based operations to better leverage factual information and improve consistency between generated text and visual inputs.
Key Takeaways
- →MAP introduces a novel map-level perspective to mitigate hallucinations in Large Vision-Language Models without requiring additional training.
- →The method interprets model hidden states as 2D semantic maps where factual information is distributed beyond localized regions.
- →Layer-Wise Criss-Cross Attention progressively refines token representations across inter- and intra-layer dimensions.
- →Global-Local Logit Fusion mechanism combines logits before and after global attention to improve prediction accuracy.
- →Consistent improvements demonstrated across benchmarks including POPE, MME, and MMHal-Bench for truthfulness and performance.
#vision-language-models#hallucination-mitigation#attention-processing#multimodal-ai#training-free#semantic-mapping#factual-consistency#lvlms
Read Original →via arXiv – CS AI
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