Summary of A Benchmark For Long-form Medical Question Answering, by Pedram Hosseini et al.
A Benchmark for Long-Form Medical Question Answering
by Pedram Hosseini, Jessica M. Sin, Bing Ren, Bryceton G. Thomas, Elnaz Nouri, Ali Farahanchi, Saeed Hassanpour
First submitted to arxiv on: 14 Nov 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 The paper introduces a new publicly available benchmark for evaluating large language models (LLMs) in long-form medical question answering. The existing benchmarks focus on automatic metrics and multiple-choice questions, which fails to capture the complexities of real-world clinical applications. The authors introduce a benchmark featuring real-world consumer medical questions with long-form answer evaluations annotated by medical doctors. They compare open-source LLMs with closed-source models based on criteria such as correctness, helpfulness, harmfulness, and bias. The preliminary results show that open LLMs have strong potential in medical QA compared to leading closed models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new benchmark for evaluating large language models (LLMs) in long-form medical question answering. This is important because current benchmarks don’t accurately test how well LLMs work in real-world situations where doctors and patients need answers to complex questions. The authors’ new benchmark has real consumer medical questions with answers evaluated by doctors, which will help improve the accuracy of LLMs for medical use. |
Keywords
» Artificial intelligence » Question answering
