Large Language Models for Few-Shot Named Entity Recognition

TL;DR

This paper introduces GPT4NER, a prompt-based method leveraging large language models for few-shot named entity recognition, transforming it into a sequence-generation task and achieving competitive results on benchmark datasets.

Contribution

The paper presents GPT4NER, a novel prompt-based approach that effectively applies LLMs to few-shot NER by converting it into a sequence-generation problem.

Findings

GPT4NER achieves 83.15% F1 on CoNLL2003.

GPT4NER outperforms few-shot baselines by 7 points.

GPT4NER attains 70.37% F1 on OntoNotes5.0.

Abstract

Named entity recognition (NER) is a fundamental task in numerous downstream applications. Recently, researchers have employed pre-trained language models (PLMs) and large language models (LLMs) to address this task. However, fully leveraging the capabilities of PLMs and LLMs with minimal human effort remains challenging. In this paper, we propose GPT4NER, a method that prompts LLMs to resolve the few-shot NER task. GPT4NER constructs effective prompts using three key components: entity definition, few-shot examples, and chain-of-thought. By prompting LLMs with these effective prompts, GPT4NER transforms few-shot NER, which is traditionally considered as a sequence-labeling problem, into a sequence-generation problem. We conduct experiments on two benchmark datasets, CoNLL2003 and OntoNotes5.0, and compare the performance of GPT4NER to representative state-of-the-art models in both few-shot and fully supervised settings. Experimental results demonstrate that GPT4NER achieves the $F_1$ of 83.15\% on CoNLL2003 and 70.37\% on OntoNotes5.0, significantly outperforming few-shot baselines by an average margin of 7 points. Compared to fully-supervised baselines, GPT4NER achieves 87.9\% of their best performance on CoNLL2003 and 76.4\% of their best performance on OntoNotes5.0. We also utilize a relaxed-match metric for evaluation and report performance in the sub-task of named entity extraction (NEE), and experiments demonstrate their usefulness to help better understand model behaviors in the NER task.