Summary of Improving Medical Reasoning Through Retrieval and Self-reflection with Retrieval-augmented Large Language Models, by Minbyul Jeong et al.
Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language Models
by Minbyul Jeong, Jiwoong Sohn, Mujeen Sung, Jaewoo Kang
First submitted to arxiv on: 27 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 introduces Self-BioRAG, a novel framework for generating explanations and retrieving domain-specific documents in the biomedical text domain. By specializing in biomedical text generation, Self-BioRAG addresses challenges posed by existing retrieval-augmented generation (RAG) methods, which often struggle with poor generalization when applied to different domain-specific problems. The proposed framework utilizes 84k filtered biomedical instruction sets and customized reflective tokens to assess generated explanations. Experimental results on three major medical question-answering benchmark datasets demonstrate significant performance gains over state-of-the-art open-foundation models, achieving a 7.2% absolute improvement on average with parameter sizes of 7B or less. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special machine that can help doctors and scientists understand complex information better. The machine is called Self-BioRAG and it’s really good at finding the right answers to tricky questions about medical topics. It works by looking through lots of documents and using its own knowledge to come up with explanations. The researchers tested this machine on many different questions and found that it was way better than other machines they tried. This is important because doctors and scientists need to be able to understand complex information quickly and accurately. |
Keywords
» Artificial intelligence » Generalization » Question answering » Rag » Retrieval augmented generation » Text generation
