Summary of Multi-modal Hallucination Control by Visual Information Grounding, By Alessandro Favero et al.
Multi-Modal Hallucination Control by Visual Information Grounding
by Alessandro Favero, Luca Zancato, Matthew Trager, Siddharth Choudhary, Pramuditha Perera, Alessandro Achille, Ashwin Swaminathan, Stefano Soatto
First submitted to arxiv on: 20 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); 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 paper investigates the issue of generative vision-language models (VLMs) producing plausible but ungrounded textual answers. It shows that this phenomenon, known as “hallucination,” arises from excessive reliance on language prior. The authors introduce a new sampling method called Multi-Modal Mutual-Information Decoding (M3ID), which amplifies the influence of the reference image over the language prior to reduce hallucinations. M3ID can be applied at inference time without retraining and with minimal overhead, or paired with Direct Preference Optimization (DPO) for improved prompt grounding during training. The paper demonstrates that M3ID and M3ID+DPO reduce hallucinated objects by 25% and 28%, respectively, while maintaining linguistic capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at a problem with computers that can understand pictures and text. Sometimes these computers give answers that are correct but not based on the picture they’re supposed to be answering about. This is called “hallucination.” The researchers found out why this happens and came up with a new way to make the computers better at understanding pictures. They call it Multi-Modal Mutual-Information Decoding (M3ID). It helps the computer focus more on what’s in the picture when giving answers, rather than just relying on what it already knows about language. This makes the computer’s answers more accurate and less likely to be completely made up. |
Keywords
* Artificial intelligence * Grounding * Hallucination * Inference * Multi modal * Optimization * Prompt
