Summary of Convis: Contrastive Decoding with Hallucination Visualization For Mitigating Hallucinations in Multimodal Large Language Models, by Yeji Park et al.
ConVis: Contrastive Decoding with Hallucination Visualization for Mitigating Hallucinations in Multimodal Large Language Models
by Yeji Park, Deokyeong Lee, Junsuk Choe, Buru Chang
First submitted to arxiv on: 25 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 paper tackles the issue of hallucinations in multimodal large language models (MLLMs), which are generated responses that fail to accurately reflect the given image. The authors introduce a novel training-free contrastive decoding method called ConVis, which leverages text-to-image generation models to semantically reconstruct images from hallucinated captions. This approach enables MLLMs to capture visual contrastive signals that penalize hallucination generation. Experiments on five popular benchmarks demonstrate the effectiveness of ConVis in reducing hallucinations across various MLLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making sure large language models don’t make up fake images when they’re supposed to describe real ones. The problem with these models is that sometimes they get confused and create images that aren’t actually there. To fix this, the authors came up with a new way of training the models so they can tell the difference between real and fake images. This method doesn’t need any extra data or updates to the models themselves. It just works within the model’s own decoding process. The results show that this approach is very effective in reducing the number of hallucinations, making these language models more reliable. |
Keywords
» Artificial intelligence » Hallucination » Image generation
