PromptNER: An Automatic Prompt-Learning Data Labeling Approach for Named Entity Recognition on Sensitive Data - M. V. de Andrade, Claudio; Muniz Belém, Fabiano; Celso França; Marcos Carvalho; Marcelo Ganem; Gabriel Texeira; Gabriel Jallais; H. F. Laender, Alberto; Marcos A. Gonçalves (Journal of Information and Data Management, 2025)
We address the task of Named Entity Recognition (NER) for entities of the types Organization and Product/Service found in textual complaints recorded on Web platforms. Due to the high inference power of Large Language Models (LLM’s), there is a growing interest in applying them to distinct problems. However, they face issues of high infrastructure cost and privacy concerns when using external API’s. Accordingly, in this article we propose PromptNER, an approach that uses LLM’s for the recognition of entities in consumers’ complaints and use them to locally train simpler models, such as SpERT (Span-based Entity and Relation Extraction Transformer), to address the task of entity and relation extraction, achieving scalabilty and privacy. Our PromptNER enhanced model achieves significant gains, between 41%-129% in F-score compared to the SpERT model trained with manually-labeled data and between 30%-268% over recent (zero-shot) Large Language Models (Llama 3.1).