Summary of A Framework For Human Evaluation Of Large Language Models in Healthcare Derived From Literature Review, by Thomas Yu Chow Tam et al.
A Framework for Human Evaluation of Large Language Models in Healthcare Derived from Literature Review
by Thomas Yu Chow Tam, Sonish Sivarajkumar, Sumit Kapoor, Alisa V Stolyar, Katelyn Polanska, Karleigh R McCarthy, Hunter Osterhoudt, Xizhi Wu, Shyam Visweswaran, Sunyang Fu, Piyush Mathur, Giovanni E. Cacciamani, Cong Sun, Yifan Peng, Yanshan Wang
First submitted to arxiv on: 4 May 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 A novel study reviews existing literature on human evaluation methodologies for large language models (LLMs) in healthcare. As LLMs increasingly impact healthcare, ensuring their safety, reliability, and effectiveness through human evaluation is crucial. However, current methods are time-consuming, non-standardized, and present significant barriers to widespread adoption. This study highlights the need for a standardized approach and proposes the QUEST framework: Quality of Information, Understanding and Reasoning, Expression Style and Persona, Safety and Harm, and Trust and Confidence. By defining clear evaluation dimensions and providing detailed guidelines, this framework aims to improve the reliability, generalizability, and applicability of human evaluation in different healthcare applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart computers that can generate text like humans do. They’re being used more in medicine, which is great! But we need a way to make sure they’re not giving us bad advice or making mistakes. Right now, people are evaluating these models by hand, but it takes forever and isn’t very scientific. This paper looks at how other researchers have been doing this evaluation and finds that there’s no one “right” way. It proposes a new system called QUEST that helps make sure evaluations are fair and accurate. |
