Summary of Comprehensive and Practical Evaluation Of Retrieval-augmented Generation Systems For Medical Question Answering, by Nghia Trung Ngo et al.
Comprehensive and Practical Evaluation of Retrieval-Augmented Generation Systems for Medical Question Answering
by Nghia Trung Ngo, Chien Van Nguyen, Franck Dernoncourt, Thien Huu Nguyen
First submitted to arxiv on: 14 Nov 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 The paper proposes a comprehensive evaluation framework for large language models (LLMs) in retrieval-augmented generation (RAG) settings, focusing on knowledge-intensive tasks from the medical domain. The Medical Retrieval-Augmented Generation Benchmark (MedRGB) is introduced, providing supplementary elements to four medical question-answering datasets for testing LLMs’ ability to handle specific scenarios. Experimental results reveal current models’ limitations in handling noise and misinformation in retrieved documents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how AI can be used to answer questions in the medical field. The researchers created a new way to test language models, making sure they can work well even when faced with tricky situations like incorrect information. They tested many different language models on this new benchmark and found that most of them have trouble dealing with errors. |
Keywords
» Artificial intelligence » Question answering » Rag » Retrieval augmented generation
