Summary of Stllava-med: Self-training Large Language and Vision Assistant For Medical Question-answering, by Guohao Sun and Can Qin and Huazhu Fu and Linwei Wang and Zhiqiang Tao
STLLaVA-Med: Self-Training Large Language and Vision Assistant for Medical Question-Answering
by Guohao Sun, Can Qin, Huazhu Fu, Linwei Wang, Zhiqiang Tao
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 This paper proposes a method called Self-Training Large Language and Vision Assistant for Medicine (STLLaVA-Med), which aims to address the issue of limited biomedical visual instruction data. By leveraging large vision-language models (LVLMs) like GPT-4o, the authors develop a policy model that can auto-generate medical visual instruction data efficiently. The proposed method involves Direct Preference Optimization (DPO) and utilizes larger LVLMs as biomedical experts to fine-tune the process. The efficacy of STLLaVA-Med is validated across three major medical Visual Question Answering (VQA) benchmarks, demonstrating competitive zero-shot performance with only 9% of the data. The authors’ approach has significant potential in assisting medical diagnosis by leveraging extensive biomedical datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better decisions when diagnosing patients. It’s hard to get the right pictures and instructions for doctors, so they can learn from them. This new method makes it easier and faster to create these images and instructions, which are really important for making good diagnoses. The method uses special computers that can understand both words and pictures, kind of like a super-smart doctor! It’s tested on three different medical challenges and does surprisingly well with only a small amount of data. |
Keywords
» Artificial intelligence » Gpt » Optimization » Question answering » Self training » Zero shot
