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        <title>Pushing the Limits of Low-Resource NER Using LLM Artificial Data Generation</title>
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    <name type="personal">
        <namePart type="given">Esther</namePart>
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    <abstract>Named Entity Recognition (NER) is an important task, but to achieve great performance, it is usually necessary to collect a large amount of labeled data, incurring high costs. In this paper, we propose using open-source Large Language Models (LLM) to generate NER data with only a few labeled examples, reducing the cost of human annotations. Our proposed method is very simple and can perform well using only a few labeled data points. Experimental results on diverse low-resource NER datasets show that our proposed data generation method can significantly improve the baseline. Additionally, our method can be used to augment datasets with class-imbalance problems and consistently improves model performance on macro-F1 metrics.</abstract>
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%0 Conference Proceedings
%T Pushing the Limits of Low-Resource NER Using LLM Artificial Data Generation
%A Santoso, Joan
%A Sutanto, Patrick
%A Cahyadi, Billy
%A Setiawan, Esther
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics: ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F santoso-etal-2024-pushing
%X Named Entity Recognition (NER) is an important task, but to achieve great performance, it is usually necessary to collect a large amount of labeled data, incurring high costs. In this paper, we propose using open-source Large Language Models (LLM) to generate NER data with only a few labeled examples, reducing the cost of human annotations. Our proposed method is very simple and can perform well using only a few labeled data points. Experimental results on diverse low-resource NER datasets show that our proposed data generation method can significantly improve the baseline. Additionally, our method can be used to augment datasets with class-imbalance problems and consistently improves model performance on macro-F1 metrics.
%R 10.18653/v1/2024.findings-acl.575
%U https://aclanthology.org/2024.findings-acl.575
%U https://doi.org/10.18653/v1/2024.findings-acl.575
%P 9652-9667

Markdown (Informal)

[Pushing the Limits of Low-Resource NER Using LLM Artificial Data Generation](https://aclanthology.org/2024.findings-acl.575) (Santoso et al., Findings 2024)

• Pushing the Limits of Low-Resource NER Using LLM Artificial Data Generation (Santoso et al., Findings 2024)

ACL

• Joan Santoso, Patrick Sutanto, Billy Cahyadi, and Esther Setiawan. 2024. Pushing the Limits of Low-Resource NER Using LLM Artificial Data Generation. In Findings of the Association for Computational Linguistics: ACL 2024, pages 9652–9667, Bangkok, Thailand. Association for Computational Linguistics.