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.

利用开源的大型语言模型 LLaMA2 作为骨干模型，并设计特定指令来区分不同类型的实体和数据集，通过将骨干模型理解指令与序列标注技术相结合，使用多个数据集来训练一个能够提取各种类型实体的模型 VANER，集成外部实体知识库和使用指令调整，通过参数的小分区训练的 VANER 明显优于之前基于大型语言模型的模型，并且作为基于大型语言模型的模型，首次超过传统的 BioNER 系统大多数最先进的模型，实现了三个数据集中最高的 F1 分数。

VANER：利用大型语言模型进行多功能和自适应的生物医学实体识别

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

Domain adaptation is an effective solution to data scarcity in low-resource
scenarios. However, when applied to token-level tasks such as bioNER, domain
adaptation methods often suffer from the challenging linguistic characteristics
that clinical narratives possess, which leads to unsatisfactory performance. In
this paper, we present a simple yet effective hardness-guided domain adaptation
(HGDA) framework for bioNER tasks that can effectively leverage the domain
hardness information to improve the adaptability of the learnt model in
low-resource scenarios. Experimental results on biomedical datasets show that
our model can achieve significant performance improvement over the recently
published state-of-the-art (SOTA) MetaNER model

本文提出了一种简单而有效的硬度引导域适应框架来提高在低资源情况下学习模型的适应性，并在生物医学数据集上进行实验证明，相较于最近发布的最先进的 MetaNER 模型，我们的模型能够显著提高性能。