Summary of Benchmarking Retrieval-augmented Generation For Medicine, by Guangzhi Xiong and Qiao Jin and Zhiyong Lu and Aidong Zhang
Benchmarking Retrieval-Augmented Generation for Medicine
by Guangzhi Xiong, Qiao Jin, Zhiyong Lu, Aidong Zhang
First submitted to arxiv on: 20 Feb 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 paper proposes a benchmark called Medical Information Retrieval-Augmented Generation Evaluation (MIRAGE) to systematically evaluate large language models (LLMs) on medical question answering tasks. MIRAGE includes 7,663 questions from five medical QA datasets and enables experiments with various combinations of corpora, retrievers, and backbone LLMs through the MedRAG toolkit. The results show that MedRAG improves the accuracy of six different LLMs by up to 18% over chain-of-thought prompting, elevating the performance of GPT-3.5 and Mixtral to GPT-4-level. The study highlights the importance of combining various medical corpora and retrievers for achieving the best performance. Additionally, it discovers log-linear scaling properties and “lost-in-the-middle” effects in medical RAG. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a new way to test language models on medical questions. They made a special dataset with 7,663 questions from five different medical areas. This allows them to compare how well the language models do when using different information sources and search methods. The results show that combining these different approaches helps language models get better answers. The study also found some surprising patterns in how language models work on medical questions. |
Keywords
» Artificial intelligence » Gpt » Prompting » Question answering » Rag » Retrieval augmented generation
