Abstract
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.
Abstract (translated)
当前解决BioNER问题的方法涉及使用表示学习和序列标注技术相结合。然而，这些方法固有地针对特定任务，表现出了差的泛化能力，通常需要为每个数据集使用专门的模型。为了利用最近在大型语言模型（LLMs）上取得的显著创新，几个研究探索了实体提取的生成方法。然而，这些方法往往无法实现前序列标注方法的效力。在本文中，我们利用开源的LLM LLaMA2作为基础模型，并针对不同类型的实体和数据集设计特定的指令。通过将LLM的指令理解与序列标注技术相结合，我们使用混合数据集来训练一个能够提取各种类型实体的模型。鉴于基础LLMs缺乏专门的医疗知识，我们还引入了外部实体知识库，并使用指令调整来促使模型对精心挑选的实体进行密集识别。我们的模型VANER，通过小参数分量训练，显著优于基于LLM的模型，并且作为基于LLM的模型，第一次超越了大多数传统BioNER系统的水平，在三个数据集上的F1得分最高。
URL
https://arxiv.org/abs/2404.17835
