Summary of Large Language Models in the Clinic: a Comprehensive Benchmark, by Fenglin Liu et al.
Large Language Models in the Clinic: A Comprehensive Benchmark
by Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xianfeng Tang, Chen Luo, Ming Zeng, Haoming Jiang, Yifan Gao, Priyanka Nigam, Sreyashi Nag, Bing Yin, Yining Hua, Xuan Zhou, Omid Rohanian, Anshul Thakur, Lei Clifton, David A. Clifton
First submitted to arxiv on: 25 Apr 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 ClinicBench, a benchmark designed to evaluate large language models (LLMs) in clinical settings. The authors collect 11 existing datasets and create six new ones that mimic real-world clinical tasks, such as open-ended decision-making and long document processing. They then evaluate the performance of 22 LLMs on these tasks under zero-shot and few-shot learning settings. Additionally, they invite medical experts to assess the clinical usefulness of LLMs. The goal is to better understand how LLMs can assist clinicians in making decisions and improving patient care. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine using computers to help doctors make better decisions about patients’ health. This paper creates a special test to see if these “smart” computers can really help doctors. They gather lots of examples from real hospitals and create new ones that mimic the kinds of questions doctors ask every day. Then, they try out many different computer programs on this test to see which ones work best. The goal is to improve how computers assist doctors and make patient care better. |
Keywords
» Artificial intelligence » Few shot » Zero shot
