Rethinking Negative Instances for Generative Named Entity Recognition
Yuyang Ding Juntao Li Pinzheng Wang Zecheng Tang Bowen Yan Min Zhang
Large Language Models (LLMs) have demonstrated impressive capabilities for
generalizing in unseen tasks. In the Named Entity Recognition (NER) task,
recent advancements have seen the remarkable improvement of LLMs in a broad
range of entity domains via instruction tuning, by adopting entity-centric
schema. In this work, we explore the potential enhancement of the existing
methods by incorporating negative instances into training. Our experiments
reveal that negative instances contribute to remarkable improvements by (1)
introducing contextual information, and (2) clearly delineating label
boundaries. Furthermore, we introduce a novel and efficient algorithm named
Hierarchical Matching, which is tailored to transform unstructured predictions
into structured entities. By integrating these components, we present GNER, a
Generative NER system that shows improved zero-shot performance across unseen
entity domains. Our comprehensive evaluation illustrates our system's
superiority, surpassing state-of-the-art (SoTA) methods by 11 $F_1$ score in
zero-shot evaluation.