GPT-NER: Named Entity Recognition via Large Language Models

TL;DR

GPT-NER transforms NER into a text generation task using large language models, achieving comparable results to supervised methods and excelling in low-resource scenarios by addressing hallucination issues with self-verification.

Contribution

This paper introduces GPT-NER, a novel approach that reformulates NER as a generation task for LLMs, bridging the performance gap and enhancing low-resource NER capabilities.

Findings

GPT-NER achieves comparable performance to supervised models on standard datasets.

GPT-NER outperforms supervised models in low-resource and few-shot settings.

Self-verification reduces hallucination issues in LLM-based NER.

Abstract

Despite the fact that large-scale Language Models (LLM) have achieved SOTA performances on a variety of NLP tasks, its performance on NER is still significantly below supervised baselines. This is due to the gap between the two tasks the NER and LLMs: the former is a sequence labeling task in nature while the latter is a text-generation model. In this paper, we propose GPT-NER to resolve this issue. GPT-NER bridges the gap by transforming the sequence labeling task to a generation task that can be easily adapted by LLMs e.g., the task of finding location entities in the input text "Columbus is a city" is transformed to generate the text sequence "@@Columbus## is a city", where special tokens @@## marks the entity to extract. To efficiently address the "hallucination" issue of LLMs, where LLMs have a strong inclination to over-confidently label NULL inputs as entities, we propose a self-verification strategy by prompting LLMs to ask itself whether the extracted entities belong to a labeled entity tag. We conduct experiments on five widely adopted NER datasets, and GPT-NER achieves comparable performances to fully supervised baselines, which is the first time as far as we are concerned. More importantly, we find that GPT-NER exhibits a greater ability in the low-resource and few-shot setups, when the amount of training data is extremely scarce, GPT-NER performs significantly better than supervised models. This demonstrates the capabilities of GPT-NER in real-world NER applications where the number of labeled examples is limited.