Research on Large Language Model-based Few-shot Medical Named Entity Recognition
Objective To use a large language model to achieve small sample medical named entity recognition. Methods Convert the medical named entity recognition task into a text generation task,construct a specific prompt template for medical named entity recognition,and enable the large language model to generate a sequence of medical entity labels during the text generation process. Retrieve a small amount of similar labeled data from medical text corpus as an example,combined with contextual learning,to achieve medical named entity recognition in small sample scenarios. Results The proposed method in this paper achieved accuracy,recall,and F1 scores of 50.54％,47.12％,and 48.77％,respectively,all of which are significantly higher than those obtained by traditional machine learning algorithms and deep learning algorithms. The reasonable use of multiple samples as examples can further enhance the model's predictive performance. Conclusion The method proposed in this paper not only does not need to update the parameters of the model,but also almost does not rely on data annotation,which improves the generalization ability of the method.
large language modelmedical named entity recognitionfew-shot
万方数据
维普
