VANER: Leveraging Large Language Model for Versatile and Adaptive Biomedical Named Entity Recognition
This work addresses the need for versatile and adaptive BioNER models that can handle multiple entity types and datasets without requiring dedicated models for each, though it is incremental as it builds on existing LLM and sequence labeling techniques.
The paper tackles the problem of poor generalizability and task-specific limitations in biomedical named entity recognition (BioNER) by combining a large language model (LLaMA2) with sequence labeling and external knowledge bases, achieving the highest F1 scores across three datasets as the first LLM-based model to surpass most conventional state-of-the-art systems.
Prevalent solution for BioNER involves using representation learning techniques coupled with sequence labeling. However, such methods are inherently task-specific, demonstrate poor generalizability, and often require dedicated model for each dataset. To leverage the versatile capabilities of recently remarkable large language models (LLMs), several endeavors have explored generative approaches to entity extraction. Yet, these approaches often fall short of the effectiveness of previouly 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 use mix of datasets to train a model 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 compel the model to densely recognize carefully curated entities. Our model VANER, trained with a small partition of parameters, significantly outperforms previous LLMs-based models and, for the first time, as a model based on LLM, surpasses the majority of conventional state-of-the-art BioNER systems, achieving the highest F1 scores across three datasets.