Summary of Development and Testing Of Retrieval Augmented Generation in Large Language Models — a Case Study Report, by Yuhe Ke et al.
Development and Testing of Retrieval Augmented Generation in Large Language Models – A Case Study Report
by YuHe Ke, Liyuan Jin, Kabilan Elangovan, Hairil Rizal Abdullah, Nan Liu, Alex Tiong Heng Sia, Chai Rick Soh, Joshua Yi Min Tung, Jasmine Chiat Ling Ong, Daniel Shu Wei Ting
First submitted to arxiv on: 29 Jan 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 Large Language Models (LLMs) have the potential to revolutionize medical applications. A novel approach called Retrieval Augmented Generation (RAG) shows promise in customizing domain knowledge within these models. This study presents a pipeline that combines LLMs and RAG, specifically designed for preoperative medicine in healthcare. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Doctors are using super smart computers to help with patient care. These computers can learn from lots of medical data and give doctors new ideas. A new way of making these computer programs even better is by adding special training just for medical cases. This helps the computers understand what they need to know about a patient before surgery. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
