Summary of Improving Retrieval-augmented Generation in Medicine with Iterative Follow-up Questions, by Guangzhi Xiong et al.
Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questions
by Guangzhi Xiong, Qiao Jin, Xiao Wang, Minjia Zhang, Zhiyong Lu, Aidong Zhang
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 study proposes a new method called iterative Retrieval-Augmented Generation for medicine (i-MedRAG) to enhance the capabilities of large language models (LLMs) in solving medical questions. The current LLMs possess considerable medical knowledge but can hallucinate and are inflexible in updating their knowledge. RAG has been proposed to address this issue, but it may still fail in complex cases requiring multiple rounds of information-seeking. i-MedRAG iteratively asks follow-up queries based on previous attempts, using a conventional RAG system to answer these queries and guide the next iteration. The experiments show improved performance for various LLMs on complex questions from clinical vignettes and knowledge tests. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about making computers better at answering medical questions. Right now, these computers can learn a lot of medical information but might make mistakes or not be able to update their knowledge well. To fix this, the researchers came up with a new way called i-MedRAG that lets computers ask follow-up questions to get more information and improve their answers. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
