GPT-NER: Named Entity Recognition via Large Language Models

By Shuhe Wang, Xiaofei Sun, Xiaoya Li, Rongbin Ouyang, Fei Wu, Tianwei Zhang, Jiwei Li, Guoyin Wang. North American Chapter of the Association for Computational Linguistics, 2023

420 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/7c9f69848d28e0a7cbb00942ee83dab9773c23e4

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Wenjun Peng
Yefeng Zheng
Wei Chen
Chao Zhang
Xiangyu Zhao
Derong Xu
Enhong Chen
Tong Xu

Citations in the corpus

Large language models for generative information extraction: a survey (2023, positive): “Considering the gap between the sequence labeling and generation models, GPT-NER [42] transformed NER into a generative task and proposed a self-verification strategy to rectify the mislabeling of NULL inputs as entities.”
Instruction Tuning for Large Language Models: A Survey (2023, neutral): “LLMs such as GPT-3 [13], PaLM [22], and LLaMA [113] have demonstrated impressive capabilities across a wide range of natural language tasks [1, 19, 38, 40, 41, 59, 60, 75, 76, 91, 104–106, 115, 117, 118, 126, 127, 135, 136, 150].”