Understanding and Mitigating Hallucinations in Multimodal Chain-of-Thought Models
- URL: http://arxiv.org/abs/2603.27201v1
- Date: Sat, 28 Mar 2026 08:56:19 GMT
- Title: Understanding and Mitigating Hallucinations in Multimodal Chain-of-Thought Models
- Abstract summary: Multimodal Chain-of-Thought (MCoT) models have demonstrated impressive capability in complex visual reasoning tasks.<n>Recent studies reveal that they suffer from severe hallucination problems due to diminished visual attention during the generation process.<n>We propose a strategy that can effectively localize divergent thinking steps and intervene in the decoding process to mitigate hallucinations.
- Score: 40.739279930631334
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Multimodal Chain-of-Thought (MCoT) models have demonstrated impressive capability in complex visual reasoning tasks. Unfortunately, recent studies reveal that they suffer from severe hallucination problems due to diminished visual attention during the generation process. However, visual attention decay is a well-studied problem in Large Vision-Language Models (LVLMs). Considering the fundamental differences in reasoning processes between MCoT models and traditional LVLMs, we raise a basic question: Whether MCoT models have unique causes of hallucinations? To answer this question, we systematically investigate the hallucination patterns of MCoT models and find that fabricated texts are primarily generated in associative reasoning steps, which we term divergent thinking. Leveraging these insights, we introduce a simple yet effective strategy that can effectively localize divergent thinking steps and intervene in the decoding process to mitigate hallucinations. Extensive experiments show that our method outperforms existing methods by a large margin. More importantly, our proposed method can be conveniently integrated with other hallucination mitigation methods and further boost their performance. The code is publicly available at https://github.com/ASGO-MM/MCoT-hallucination.
Related papers
- Hallucination-aware intermediate representation edit in large vision-language models [46.189518549824356]
Large Vision-Language Models have demonstrated exceptional performance in multimodal reasoning and complex scene understanding.<n>Recent research on hallucination mitigation has focused on retraining methods and Contrastive Decoding (CD) methods.<n>We propose a framework for dynamically detecting hallucination representations and performing hallucination-eliminating edits.
arXiv Detail & Related papers (2026-03-31T08:10:13Z) - Seeing Through the Chain: Mitigate Hallucination in Multimodal Reasoning Models via CoT Compression and Contrastive Preference Optimization [78.94590726578014]
multimodal reasoning models (MLRMs) remain prone to hallucinations, and effective solutions are still underexplored.<n>We propose C3PO, a training-based mitigation framework comprising textbfCompression and textbfPreference textbfOptimization.
arXiv Detail & Related papers (2026-02-03T11:00:55Z) - MM-THEBench: Do Reasoning MLLMs Think Reasonably? [45.23711313374087]
We introduce MM-THEBench, a comprehensive benchmark for assessing hallucinations of intermediate CoTs in reasoning MLLMs.<n> MM-THEBench features a fine-grained taxonomy grounded in cognitive dimensions, diverse data with verified reasoning annotations, and a multi-level automated evaluation framework.
arXiv Detail & Related papers (2026-01-30T09:17:50Z) - 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) - MIRAGE: Assessing Hallucination in Multimodal Reasoning Chains of MLLM [58.2298313720146]
Multimodal hallucinations are multi-sourced and arise from diverse causes.<n>Existing benchmarks fail to adequately distinguish between perception-induced hallucinations and reasoning-induced hallucinations.
arXiv Detail & Related papers (2025-05-30T05:54:36Z) - Combating Multimodal LLM Hallucination via Bottom-Up Holistic Reasoning [151.4060202671114]
multimodal large language models (MLLMs) have shown unprecedented capabilities in advancing vision-language tasks.<n>This paper introduces a novel bottom-up reasoning framework to address hallucinations in MLLMs.<n>Our framework systematically addresses potential issues in both visual and textual inputs by verifying and integrating perception-level information with cognition-level commonsense knowledge.
arXiv Detail & Related papers (2024-12-15T09:10:46Z) - Thinking Before Looking: Improving Multimodal LLM Reasoning via Mitigating Visual Hallucination [13.706325901731665]
Multimodal large language models (MLLMs) have advanced the integration of visual and linguistic modalities.
Current approaches like chain of thought (CoT) reasoning have augmented the cognitive capabilities of large language models (LLMs)
But their adaptation to MLLMs is hindered by heightened risks of hallucination in cross-modality comprehension.
arXiv Detail & Related papers (2024-11-15T21:01:37Z) - 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) - 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)
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.