Enhancing Biomedical NER with Adversarial Selective Training

Large language models (LLMs) have significantly impacted the field of natural language processing (NLP). However, due to the limited domain specificity of the training data and the model’s constrained ability to generalize across complex biomedical data, LLMs continue to encounter challenges related to prediction bias and low generalization in biomedical named entity recognition (BioNER). In this work, we set out to improve the recognition and generalization capabilities of LLMs in BioNER through an Adversarial Selective Training (AST) method. Our method maximizes the adversarial loss to obtain the importance ranking of weights, which guides the model to selectively train to generate counterfactual examples. This strategy aims to force the model to explore the amount of information in the latent space to extract entities, thereby improving the performance of BioNER. Specifically, we conduct in-distribution experiments on five biomedical datasets and out-of-distribution experiments on two datasets. Experimental results show that our method outperforms other LLMs-based methods and significantly improves the performance of BioNER.