Hugging Face Trending Papers
Beyond Attention Imbalance: Mitigating Hallucinations via Spectral Surgery
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arXiv Computer Vision
4d ago
Beyond Attention Imbalance: Mitigating Hallucinations via Spectral Surgery
arXiv:2609.37263v1 Announce Type: new Abstract: While Large Vision-Language Models (LVLMs) achieve remarkable success, hallucinations remain a significant barrier to their reliable deployment. Recent...
arXiv AI
Jun 30
FADE: Mitigating Hallucinations by Reducing Language-Prior Dominance in Large Vision-Language Models
arXiv:2606. 29431v1 Announce Type: new Abstract: Despite the impressive capabilities of Large Vision-Language Models (LVLMs), they remain susceptible to hallucination, generating content inconsistent with the input image.
arXiv AI
Jun 4
Correcting Visual Blur Induced by Attention Distraction to Reduce Hallucinations: Algorithm and Theory
arXiv:2605. 24602v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) frequently suffer from object hallucinations, yet the visual perceptual mechanism underlying this failure remains poorly understood.
arXiv AI
Jun 16
Mitigating Object Hallucinations in LVLMs via Attention Imbalance Rectification
arXiv:2603. 24058v2 Announce Type: replace-cross Abstract: Object hallucination in Large Vision-Language Models (LVLMs) severely compromises their reliability in real-world applications, posing a critical barrier to their deployment in high-stakes scenarios such as autonomous driving and medical image analysis.
arXiv Machine Learning
Jun 9
Steer Where It Matters: Token-Level Visual-Sensitivity Steering for LVLMs Hallucination Mitigation
arXiv:2606. 07647v1 Announce Type: cross Abstract: Large vision language models (LVLMs) have made rapid advancements and are deployed across various applications, yet hallucinations remain a major challenge.
arXiv AI
Sep 2
Revealing Multi-View Hallucination in Large Vision-Language Models
The paper introduces the concept of multi-view hallucination (MVH), where large vision-language models produce incorrect answers when processing images from multiple viewpoints. It presents MVH-Bench, a benchmark of 4.8k question-answer pairs that target cross-instance and cross-view hallucinations, and shows that MVH is common across recent models. The authors propose Reference Shift Contrastive Decoding (RSCD), a training-free decoding method that mitigates visual interference, achieving significant performance gains on MVH-Bench with LLaVA-OneVision and Qwen2.5-VL.