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.

翻译：尽管大规模语言模型（LLM）已在多种自然语言处理任务中取得最优性能，其在命名实体识别（NER）任务上的表现仍显著低于有监督基线方法。这一差距源于二者本质差异：NER本质上是序列标注任务，而LLM属于文本生成模型。本文提出GPT-NER以解决此问题。GPT-NER通过将序列标注任务转化为LLM易于适应的生成任务来弥合差距，例如将输入文本"Columbus is a city"中查找地点实体的任务，转化为生成文本序列"@@Columbus## is a city"，其中特殊标记@@和##标识待提取实体。为有效应对LLM的"幻觉"问题——即模型倾向于过度自信地将空输入标注为实体——我们提出自验证策略，通过引导LLM自问自答，判断提取的实体是否属于标注的实体标签。我们在五个广泛采用的NER数据集上进行实验，结果显示GPT-NER达到了与全监督基线方法相当的性能——据我们所知，这是首次实现该突破。更重要的是，我们发现GPT-NER在低资源和少样本场景下展现出更强的能力：当训练数据极度稀缺时，GPT-NER的性能显著优于监督模型。这表明GPT-NER在标注样本受限的真实NER应用中具有巨大潜力。