Summary of A Survey on Large Language Models From General Purpose to Medical Applications: Datasets, Methodologies, and Evaluations, by Jinqiang Wang et al.
A Survey on Large Language Models from General Purpose to Medical Applications: Datasets, Methodologies, and Evaluations
by Jinqiang Wang, Huansheng Ning, Yi Peng, Qikai Wei, Daniel Tesfai, Wenwei Mao, Tao Zhu, Runhe Huang
First submitted to arxiv on: 14 Jun 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 presents a comprehensive guide to training Large Language Models (LLMs) specifically designed for medical applications. Building upon open-source general LLMs requires fewer computational resources and ensures better patient privacy protection compared to API-based solutions. The survey provides fine-grained insights into acquiring training corpora, constructing customized medical training sets, selecting suitable training paradigms, and choosing evaluation benchmarks. It also discusses existing challenges and promising research directions for medical applications such as medical education, diagnostic planning, and clinical assistants. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to make special computers that can talk like doctors. These computers are trained using data from the internet, but they’re made better by adding information specific to medicine. This makes them good at giving medical advice and helping patients. The paper shows us how to make these computers work best for different medical uses. |
