Summary of Relation Extraction Using Large Language Models: a Case Study on Acupuncture Point Locations, by Yiming Li et al.
Relation Extraction Using Large Language Models: A Case Study on Acupuncture Point Locations
by Yiming Li, Xueqing Peng, Jianfu Li, Xu Zuo, Suyuan Peng, Donghong Pei, Cui Tao, Hua Xu, Na Hong
First submitted to arxiv on: 8 Apr 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 compares the performance of large language models (LLMs) like Generative Pre-trained Transformers (GPT) with traditional deep learning models in extracting relations related to acupoint locations from textual knowledge sources. The study aims to assess the impact of pretraining and fine-tuning on GPT’s performance. Five types of relations between acupoints were annotated, and five models were compared: BioBERT, LSTM, pre-trained GPT-3.5, fine-tuned GPT-3.5, and pre-trained GPT-4. The results show that fine-tuned GPT-3.5 consistently outperformed other models in F1 scores across all relation types. The study highlights the effectiveness of LLMs like GPT in extracting relations related to acupoint locations, with implications for accurately modeling acupuncture knowledge and promoting standard implementation in acupuncture training and practice. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper compares different AI models’ ability to understand how to locate specific points on the body that help with acupuncture. It uses a big database of information about these points to test which model is best at finding relationships between them. The study found that one model, called GPT-3.5, was the best at this task when it was trained and fine-tuned just right. This could be helpful for people learning about acupuncture and for doctors using computers to help their patients. |
Keywords
» Artificial intelligence » Deep learning » Fine tuning » Gpt » Lstm » Pretraining
