Thinking Before Looking: Improving Multimodal LLM Reasoning via Mitigating Visual Hallucination

• URL: http://arxiv.org/abs/2411.12591v1
• Date: Fri, 15 Nov 2024 21:01:37 GMT
• Title: Thinking Before Looking: Improving Multimodal LLM Reasoning via Mitigating Visual Hallucination
• Authors: Haojie Zheng, Tianyang Xu, Hanchi Sun, Shu Pu, Ruoxi Chen, Lichao Sun,
• Abstract summary: 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.
• Score: 13.706325901731665
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• Abstract: Multimodal large language models (MLLMs) have advanced the integration of visual and linguistic modalities, establishing themselves as the dominant paradigm for visual-language tasks. Current approaches like chain of thought (CoT) reasoning have augmented the cognitive capabilities of large language models (LLMs), yet their adaptation to MLLMs is hindered by heightened risks of hallucination in cross-modality comprehension. In this paper, we find that the thinking while looking paradigm in current multimodal CoT approaches--where reasoning chains are generated alongside visual input--fails to mitigate hallucinations caused by misleading images. To address these limitations, we propose the Visual Inference Chain (VIC) framework, a novel approach that constructs reasoning chains using textual context alone before introducing visual input, effectively reducing cross-modal biases and enhancing multimodal reasoning accuracy. Comprehensive evaluations demonstrate that VIC significantly improves zero-shot performance across various vision-related tasks, mitigating hallucinations while refining the reasoning capabilities of MLLMs. Our code repository can be found at https://github.com/Terry-Xu-666/visual_inference_chain.

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