Summary of Rule: Reliable Multimodal Rag For Factuality in Medical Vision Language Models, by Peng Xia and Kangyu Zhu and Haoran Li and Hongtu Zhu and Yun Li and Gang Li and Linjun Zhang and Huaxiu Yao
RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models
by Peng Xia, Kangyu Zhu, Haoran Li, Hongtu Zhu, Yun Li, Gang Li, Linjun Zhang, Huaxiu Yao
First submitted to arxiv on: 6 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY)
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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 Medical Large Vision Language Models (Med-LVLMs) have revolutionized medical diagnosis, but they often produce inaccurate responses due to factual issues. Retrieval-Augmented Generation (RAG) can improve accuracy by incorporating external knowledge, but it introduces two key challenges: limited or excessive retrieved contexts that may interfere with the model’s generation. To address these issues, we propose RULE, a framework comprising two components. The first component controls factuality risk through calibrated context selection, while the second fine-tunes the model to balance its dependence on inherent knowledge and retrieved contexts for generation. We demonstrate the effectiveness of RULE on medical VQA and report generation tasks across three datasets, achieving an average improvement of 47.4% in factual accuracy. Our framework utilizes RAG to improve Med-LVLMs’ factual accuracy while minimizing risks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making language models better at giving correct answers when they’re used for medical diagnosis. Right now, these models often make mistakes because they don’t have the right information. One way to fix this is by letting them look at external knowledge, but that can also cause problems if they rely too much on what they find. The researchers propose a new approach called RULE that helps control how much external knowledge the model uses and balances it with what it already knows. They tested this approach on three different datasets and saw a big improvement in getting accurate answers. |
Keywords
* Artificial intelligence * Rag * Retrieval augmented generation
