HIME: Mitigating Object Hallucinations in LVLMs via Hallucination Insensitivity Model Editing

• URL: http://arxiv.org/abs/2602.18711v1
• Date: Sat, 21 Feb 2026 04:16:17 GMT
• Title: HIME: Mitigating Object Hallucinations in LVLMs via Hallucination Insensitivity Model Editing
• Authors: Ahmed Akl, Abdelwahed Khamis, Ali Cheraghian, Zhe Wang, Sara Khalifa, Kewen Wang,
• Abstract summary: Large Vision-Language Models (LVLMs) have demonstrated impressive multimodal understanding capabilities.<n>LVLMs are prone to object hallucination, where models describe non-existent objects or attribute incorrect factual information.<n>We propose Hallucination Insensitivity Model Editing (HIME), a layer-adaptive weight editing approach that selectively modifies latent features to suppress hallucinations.
• Score: 6.021803204524807
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Vision-Language Models (LVLMs) have demonstrated impressive multimodal understanding capabilities, yet they remain prone to object hallucination, where models describe non-existent objects or attribute incorrect factual information, raising serious concerns for reliable real-world deployment. While fine-tuning is a commonly adopted mitigation strategy, its high computational cost and practical difficulty motivate the need for training-free alternatives, among which model editing has recently emerged as a promising direction. However, indiscriminate editing risks disrupting the rich implicit knowledge encoded in pre-trained LVLMs, leading to a fundamental question: how much intervention is necessary at each layer to suppress hallucinations while preserving pre-trained knowledge? To address this question, we present a systematic analysis of LVLM decoders built on three widely used large language model backbones-Qwen, LLaMA, and Vicuna-revealing clear layer-wise differences in susceptibility to object hallucination. Building on these insights, we introduce the Hallucination Insensitivity Score (HIS), a principled metric that quantifies each layer's sensitivity to hallucination and provides guidance for targeted intervention. Leveraging HIS, we propose Hallucination Insensitivity Model Editing (HIME), a simple yet effective layer-adaptive weight editing approach that selectively modifies latent features to suppress hallucinations while preserving pre-trained knowledge. Extensive experiments demonstrate that HIME reduces hallucinations by an average of 61.8% across open-ended generation benchmarks, including CHAIR, MME, and GPT-4V-aided evaluation, without introducing additional parameters, inference-time latency, or computational overhead.

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