Summary of Thinking Before Looking: Improving Multimodal Llm Reasoning Via Mitigating Visual Hallucination, by Haojie Zheng et al.
Thinking Before Looking: Improving Multimodal LLM Reasoning via Mitigating Visual Hallucination
by Haojie Zheng, Tianyang Xu, Hanchi Sun, Shu Pu, Ruoxi Chen, Lichao Sun
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Visual Inference Chain (VIC) framework enhances multimodal reasoning accuracy in large language models by constructing reasoning chains using textual context alone before introducing visual input, thus reducing cross-modal biases and hallucinations. Building upon current multimodal chain of thought (CoT) approaches, VIC’s novel paradigm improves zero-shot performance across various vision-related tasks while refining the cognitive capabilities of multimodal large language models. The paper demonstrates significant improvements in multimodal reasoning accuracy through comprehensive evaluations, showcasing the potential for VIC to mitigate hallucinations and enhance the capabilities of these models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to understand a picture by reading about it first. Then, when you see the actual image, your understanding improves. This is what the Visual Inference Chain (VIC) framework does, but with machines learning instead of humans. Current methods for combining words and pictures haven’t been working well because they can get confused by misleading images. VIC solves this problem by thinking about the text before seeing the picture, which helps it understand better. The results show that VIC is much better at understanding pictures and improving its reasoning skills. |
Keywords
» Artificial intelligence » Inference » Zero shot
