Summary of Self-introspective Decoding: Alleviating Hallucinations For Large Vision-language Models, by Fushuo Huo et al.
Self-Introspective Decoding: Alleviating Hallucinations for Large Vision-Language Models
by Fushuo Huo, Wenchao Xu, Zhong Zhang, Haozhao Wang, Zhicheng Chen, Peilin Zhao
First submitted to arxiv on: 4 Aug 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 Self-Introspective Decoding (SID) method addresses the issue of hallucination in Large Vision-Language Models (LVLMs). The existing methods to mitigate this problem, such as robust instruction tuning and contrastive decoding, incur additional costs or double inference time. SID assesses the importance of vision tokens based on preceding vision and text tokens, and uses the Context and Text-aware Token Selection (CT2S) strategy to preserve only unimportant vision tokens after early layers. This approach reduces hallucination and improves text quality without extra knowledge or computation burdens. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new method called Self-Introspective Decoding (SID) that helps Large Vision-Language Models (LVLMs) avoid making mistakes when generating texts. The current methods to fix this issue require extra information or take longer to process. SID looks at the importance of visual and text information and keeps only what’s needed, allowing LVLMs to generate better texts without getting confused. |
Keywords
» Artificial intelligence » Hallucination » Inference » Instruction tuning » Token
