Summary of Intelligent Understanding Of Large Language Models in Traditional Chinese Medicine Based on Prompt Engineering Framework, by Yirui Chen et al.
Intelligent Understanding of Large Language Models in Traditional Chinese Medicine Based on Prompt Engineering Framework
by Yirui Chen, Qinyu Xiao, Jia Yi, Jing Chen, Mengyang Wang
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes a framework called TCM-Prompt to enhance the performance of large language models (LLMs) in Traditional Chinese Medicine (TCM). It combines pre-trained language models, templates, tokenization, and verbalization methods to create customized models for specific TCM tasks. The authors conducted experiments on disease classification, syndrome identification, herbal medicine recommendation, and general NLP tasks, showing that their approach outperforms baseline methods. The results suggest that prompt engineering can improve LLMs’ performance in specialized domains like TCM, with potential applications in digitalization, modernization, and personalized medicine. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using special techniques to make language models better for a specific area called Traditional Chinese Medicine. They created a new way to combine different language models and techniques to help doctors and researchers use computers more effectively. The authors tested their method on several tasks and showed that it works better than usual. This could be important for things like creating personalized medicine and making healthcare more digital. |
Keywords
» Artificial intelligence » Classification » Nlp » Prompt » Tokenization
