@inproceedings{wang-etal-2025-gpt,
    title = "{GPT}-{NER}: Named Entity Recognition via Large Language Models",
    author = "Wang, Shuhe  and
      Sun, Xiaofei  and
      Li, Xiaoya  and
      Ouyang, Rongbin  and
      Wu, Fei  and
      Zhang, Tianwei  and
      Li, Jiwei  and
      Wang, Guoyin  and
      Guo, Chen",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.findings-naacl.239/",
    pages = "4257--4275",
    ISBN = "979-8-89176-195-7",
    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 {\textquotedblleft}Columbus is a city{\textquotedblright} is transformed to generate the text sequence ``@@Columbus{\#}{\#} is a city{\textquotedblright}, where special tokens @@{\#}{\#} marks the entity to extract. To efficiently address the \textit{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."
}