Exposing Hallucinations To Suppress Them: VLMs Representation Editing With Generative Anchors

• URL: http://arxiv.org/abs/2509.21997v1
• Date: Fri, 26 Sep 2025 07:24:28 GMT
• Title: Exposing Hallucinations To Suppress Them: VLMs Representation Editing With Generative Anchors
• Authors: Youxu Shi, Suorong Yang, Dong Liu,
• Abstract summary: Multimodal large language models (MLLMs) have achieved remarkable success across diverse vision-language tasks.<n>MLLMs are highly susceptible to hallucinations, producing content that is fluent but inconsistent with visual evidence.<n>We propose a training-free, self-supervised method for hallucination mitigation.
• Score: 8.089908150148554
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal large language models (MLLMs) have achieved remarkable success across diverse vision-language tasks, yet they remain highly susceptible to hallucinations, producing content that is fluent but inconsistent with visual evidence. Such hallucinations, spanning objects, attributes, and relations, persist even in larger models, while existing mitigation approaches often require additional finetuning, handcrafted priors, or trade-offs that compromise informativeness and scalability. To address this limitation, we propose a training-free, self-supervised method for hallucination mitigation. Our approach introduces a novel hallucination amplification mechanism: a caption is projected into the visual space via a text-to-image model to reveal implicit hallucination signals, serving as a negative anchor, while the original image provides a positive anchor. Leveraging these dual anchors, we edit decoder hidden states by pulling representations toward faithful semantics and pushing them away from hallucination directions. This correction requires no human priors or additional training costs, ensuring both effectiveness and efficiency. Extensive experiments across multiple benchmarks show that our method significantly reduces hallucinations at the object, attribute, and relation levels while largely preserving recall and caption richness, e.g., achieving a hallucination reduction by over 5% using LLaVA-v1.5-7B on CHAIR. Furthermore, results on diverse architectures, including LLaVA-NEXT-7B, Cambrian-8B, and InstructBLIP-7B, validate strong cross-architecture generalization. More importantly, when applied to hallucination-free captions, our method introduces almost no side effects, underscoring its robustness and practical plug-and-play applicability. The implementation will be publicly available.

Related papers

• AdaIAT: Adaptively Increasing Attention to Generated Text to Alleviate Hallucinations in LVLM [28.68190828797428]
  We propose Attention to Generated Text (IAT) to alleviate hallucinations while avoiding repetitive descriptions.<n>AdaIAT employs a layer-wise threshold to control intervention time and fine-grained amplification magnitude tailored to the characteristics of each attention head.
  arXiv  Detail & Related papers  (2026-03-05T07:52:11Z)
• CRoPS: A Training-Free Hallucination Mitigation Framework for Vision-Language Models [3.0991186209192794]
  Hallucinated content undermines reliability in real-world use.<n>New model captures hallucination effects by selectively removing key text tokens.<n>Generalized Contrastive Decoding integrates multiple hallucinated models to represent diverse hallucination sources.
  arXiv  Detail & Related papers  (2026-01-02T11:39:00Z)
• PruneHal: Reducing Hallucinations in Multi-modal Large Language Models through Adaptive KV Cache Pruning [87.35309934860938]
  hallucinations in large language models (MLLMs) are strongly associated with insufficient attention allocated to visual tokens.<n>We propose textbfPruneHal, a training-free, simple yet effective method that leverages adaptive KV cache pruning to enhance the model's focus on critical visual information.
  arXiv  Detail & Related papers  (2025-10-22T02:41:07Z)
• CAI: Caption-Sensitive Attention Intervention for Mitigating Object Hallucination in Large Vision-Language Models [60.0300765815417]
  Large Vision-Language Models (LVLMs) frequently produce content that deviates from visual information, leading to object hallucination.<n>We propose Caption-sensitive Attention Intervention (CAI), a training-free, plug-and-play hallucination mitigation method.
  arXiv  Detail & Related papers  (2025-06-30T07:52:36Z)
• Generate, but Verify: Reducing Hallucination in Vision-Language Models with Retrospective Resampling [67.14942827452161]
  Vision-Language Models (VLMs) excel at visual understanding but often suffer from visual hallucinations.<n>In this work, we introduce REVERSE, a unified framework that integrates hallucination-aware training with on-the-fly self-verification.
  arXiv  Detail & Related papers  (2025-04-17T17:59:22Z)
• PerturboLLaVA: Reducing Multimodal Hallucinations with Perturbative Visual Training [56.172959986096316]
  This paper aims to address the challenge of hallucinations in Multimodal Large Language Models (MLLMs)<n>HalFscore is a novel metric built upon the language graph and is designed to evaluate both the accuracy and completeness of dense captions at a granular level.<n>PerturboLLaVA significantly improves the fidelity of generated captions, outperforming existing approaches in handling multimodal hallucinations.
  arXiv  Detail & Related papers  (2025-03-09T07:07:03Z)
• VaLiD: Mitigating the Hallucination of Large Vision Language Models by Visual Layer Fusion Contrastive Decoding [38.23310445372371]
  Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in multimodal task reasoning.<n>They often generate responses that appear plausible yet do not accurately reflect the visual content, a phenomenon known as hallucination.<n>Recent approaches have introduced training-free methods to mitigate hallucinations by adjusting the decoding strategy during the inference stage.<n>We propose a novel hallucination-mitigation method from the visual encoding perspective: textbfVisutextbfal textbfLayer Fustextbfion Contrastive textbfD
  arXiv  Detail & Related papers  (2024-11-24T13:42:02Z)
• HELPD: Mitigating Hallucination of LVLMs by Hierarchical Feedback Learning with Vision-enhanced Penalty Decoding [36.360171373963716]
  Large Vision-Language Models (LVLMs) have shown remarkable performance on many visual-language tasks. These models still suffer from multimodal hallucination, which means the generation of objects or content that violates the images. We propose Hierarchical Feedback Learning with Vision-enhanced Penalty Decoding (HELPD) to address this issue.
  arXiv  Detail & Related papers  (2024-09-30T15:52:05Z)
• Alleviating Hallucinations in Large Vision-Language Models through Hallucination-Induced Optimization [123.54980913741828]
  Large Visual Language Models (LVLMs) have demonstrated exceptional abilities in understanding multimodal data.<n>They invariably suffer from hallucinations, leading to a disconnect between the generated text and the corresponding images.<n>Almost all current visual contrastive decoding methods attempt to mitigate these hallucinations by introducing visual uncertainty information.<n>However, they struggle to precisely induce the hallucinatory tokens, which severely limits their effectiveness in mitigating hallucinations.
  arXiv  Detail & Related papers  (2024-05-24T08:46:31Z)
• Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback [40.930238150365795]
  We propose detecting and mitigating hallucinations in Large Vision Language Models (LVLMs) via fine-grained AI feedback.<n>We generate a small-size hallucination annotation dataset by proprietary models.<n>Then, we propose a detect-then-rewrite pipeline to automatically construct preference dataset for training hallucination mitigating model.
  arXiv  Detail & Related papers  (2024-04-22T14:46:10Z)
• Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding [25.489832294197797]
  This paper introduces the Instruction Contrastive Decoding (ICD) method, a novel approach designed to reduce hallucinations during LVLM inference. Our method is inspired by our observation that what we call disturbance instructions significantly exacerbate hallucinations in multimodal fusion modules.
  arXiv  Detail & Related papers  (2024-03-27T16:04:47Z)
• Hallucination Augmented Contrastive Learning for Multimodal Large Language Model [53.65682783591723]
  Multi-modal large language models (MLLMs) have been shown to efficiently integrate natural language with visual information to handle multi-modal tasks. However, MLLMs still face a fundamental limitation of hallucinations, where they tend to generate erroneous or fabricated information. In this paper, we address hallucinations in MLLMs from a novel perspective of representation learning.
  arXiv  Detail & Related papers  (2023-12-12T04:05:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.