Improving large language models for clinical named entity recognition via prompt engineering.
The study highlights the potential of large language models, specifically GPT-3.5 and GPT-4, in processing complex clinical data and extracting meaningful information with minimal training data. By developing and refining prompt-based strategies, we can significantly enhance the models' performance, making them viable tools for clinical NER tasks and possibly reducing the reliance on extensive annotated datasets.
Author(s): Hu, Yan, Chen, Qingyu, Du, Jingcheng, Peng, Xueqing, Keloth, Vipina Kuttichi, Zuo, Xu, Zhou, Yujia, Li, Zehan, Jiang, Xiaoqian, Lu, Zhiyong, Roberts, Kirk, Xu, Hua
DOI: 10.1093/jamia/ocad259