Summary of Mitigating Hallucinations in Large Vision-language Models Via Summary-guided Decoding, by Kyungmin Min et al.
Mitigating Hallucinations in Large Vision-Language Models via Summary-Guided Decoding
by Kyungmin Min, Minbeom Kim, Kang-il Lee, Dongryeol Lee, Kyomin Jung
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 method, Summary-Guided Decoding (SumGD), tackles the issue of language priors in Large Vision-Language Models (LVLMs) by reducing text context through summaries. This approach naturally encourages LVLMs to focus on image information, while controlling only image-related part-of-speech (POS) tokens to maintain text quality. SumGD achieves state-of-the-art performance on object hallucination benchmarks and demonstrates robustness in balancing the reduction of object hallucinations with maintaining text quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Vision-Language Models can create detailed responses from images, but they might make things up because they rely too much on language. Researchers looked at how these models work and found two main problems: 1) as they generate more words, they start relying more on language, which makes them invent things; and 2) methods that try to fix this problem can actually make things worse or decrease the quality of the text. To solve this issue, a new method called Summary-Guided Decoding (SumGD) was developed. This approach helps models focus on the image by using summaries instead of full text, which maintains good text quality and reduces inventing. |
Keywords
» Artificial intelligence » Hallucination
