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上取得最优性能，但其在命名实体识别（NER）任务上的表现仍显著低于监督基线方法。这源于NER与LLM之间的任务本质差异：前者是序列标注任务，而后者是文本生成模型。本文提出GPT-NER以解决该问题。GPT-NER通过将序列标注任务转化为LLM易于适配的生成任务来弥合这一差距——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@@##标注待提取实体。针对LLM易过度自信地将空输入标记为实体的"幻觉"问题，我们提出自验证策略：通过提示LLM自我询问提取的实体是否属于标注的实体标签。在五个广泛使用的NER数据集上的实验表明，GPT-NER首次达到了与完全监督基线相当的性能。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能力——当训练数据极度稀缺时，GPT-NER的表现显著优于监督模型。这证明了GPT-NER在标注样本有限的实际NER应用中的潜力。