Summary of Evaluation Of General Large Language Models in Contextually Assessing Semantic Concepts Extracted From Adult Critical Care Electronic Health Record Notes, by Darren Liu et al.
Evaluation of General Large Language Models in Contextually Assessing Semantic Concepts Extracted from Adult Critical Care Electronic Health Record Notes
by Darren Liu, Cheng Ding, Delgersuren Bold, Monique Bouvier, Jiaying Lu, Benjamin Shickel, Craig S. Jabaley, Wenhui Zhang, Soojin Park, Michael J. Young, Mark S. Wainwright, Gilles Clermont, Parisa Rashidi, Eric S. Rosenthal, Laurie Dimisko, Ran Xiao, Joo Heung Yoon, Carl Yang, Xiao Hu
First submitted to arxiv on: 24 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
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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 explores the application of Large Language Models (LLMs) in adult critical care medicine, evaluating their performance in understanding and processing real-world clinical notes. The study uses systematic and comprehensible analytic methods, including clinician annotation and adjudication, to assess the proficiency of three general LLMs: GPT-4, GPT-3.5, and text-davinci-003. The results show that GPT-4 demonstrated superior performance overall, while GPT-3.5 and text-davinci-003 performed better with specific prompting strategies. The study highlights the need for a comprehensive qualitative performance evaluation framework for LLMs, which can validate their capabilities in processing complex medical data and establish a benchmark for future evaluations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well Large Language Models (LLMs) can understand and work with real medical notes from doctors. They tested three different LLMs to see how good they are at understanding what’s written in these notes. The results show that one of the LLMs, called GPT-4, is really good at doing this job. The other two LLMs were not as good, but if you use them in a special way, they can do okay too. This study shows us why we need to test LLMs more carefully and make sure they’re working well in real-life medical situations. |
Keywords
» Artificial intelligence » Gpt » Prompting
