Summary of Evaluation Of Large Language Models For Summarization Tasks in the Medical Domain: a Narrative Review, by Emma Croxford et al.
Evaluation of Large Language Models for Summarization Tasks in the Medical Domain: A Narrative Review
by Emma Croxford, Yanjun Gao, Nicholas Pellegrino, Karen K. Wong, Graham Wills, Elliot First, Frank J. Liao, Cherodeep Goswami, Brian Patterson, Majid Afshar
First submitted to arxiv on: 26 Sep 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 approach in large language models has led to significant advancements in natural language generation for medical purposes, allowing for efficient management of vast amounts of medical text. However, the high-stakes nature of medicine necessitates reliable evaluation methods, which remains a significant challenge. This narrative review aims to evaluate the current state-of-the-art in clinical summarization task evaluation and propose future directions to address the limitations of expert human evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical text is overwhelming, but AI can help! Researchers have made huge progress in using language models for medical writing. The problem is that we need a way to check if it’s good or not – like having experts read it over. This paper looks at how well we’re doing with this “evaluation” and what we should do next. |
Keywords
» Artificial intelligence » Summarization
