VANER: Leveraging Large Language Model for Versatile and Adaptive Biomedical Named Entity Recognition

Junyi Bian, Weiqi Zhai, Xiaodi Huang, Jiaxuan Zheng, Shanfeng Zhu

Abstract: The prevalent solution for BioNER involves using representation learning techniques combined with sequence labeling. However, such methods are inherently task-specific, demonstrate poor generalizability, and often require a dedicated model for each dataset. To leverage the versatile capabilities of recent large language models (LLMs), several approaches have explored generative techniques for entity extraction. Yet, these approaches often fall short compared to previous sequence labeling approaches. In this paper, we utilize the open-sourced LLM LLaMA2 as the backbone model, and design specific instructions to distinguish between different types of entities and datasets. By combining the LLM’s understanding of instructions with sequence labeling techniques, we train a model using a mix of datasets capable of extracting various types of entities. Given that the backbone LLMs lacks specialized medical knowledge, we also integrate external entity knowledge bases and employ instruction tuning to enable the model to densely recognize curated entities. Our parameter-efficient training model, VANER, significantly outperforms previous LLMs-based models. For the first time, as an LLM-based model, VANER surpasses the majority of conventional state-of-the-art BioNER systems, achieving the highest F1 scores across three datasets.