@inproceedings{liu-etal-2025-improving,
title = "Improving Data Annotation for Low-Resource Relation Extraction with Logical Rule-Augmented Collaborative Language Models",
author = "Liu, Xiyang and
Hu, Chunming and
Zhang, Richong and
Chen, Junfan and
Xu, Baowen",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.70/",
doi = "10.18653/v1/2025.naacl-long.70",
pages = "1497--1510",
ISBN = "979-8-89176-189-6",
abstract = "Low-resource relation extraction aims to identify semantic relationships between entities using scarce labeled data. Recent studies exploit large language models to recognize relations based on retrieved examplars, yielding promising results. However, the reliability of predictions from these methods is constrained by the presence of irrelevant context within demonstrations and the inherent flaws of large language models in producing undesired outputs. Inspired by the precision and generalization of abstract logic, in this paper, we propose distilling logical rules to uniformly represent task knowledge sourced from distinct origins and facilitate deductive reasoning. We develop a collaborative annotating framework that iteratively integrates high-confidence predictions of rule-enhanced relation extractors with varying scales, efficiently obtaining reliable pseudo annotations from massive unlabeled samples without human supervision. Experiments under two inference settings show that our approach achieves new state-of-the-art performance on benchmark datasets in few-shot scenarios."
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<abstract>Low-resource relation extraction aims to identify semantic relationships between entities using scarce labeled data. Recent studies exploit large language models to recognize relations based on retrieved examplars, yielding promising results. However, the reliability of predictions from these methods is constrained by the presence of irrelevant context within demonstrations and the inherent flaws of large language models in producing undesired outputs. Inspired by the precision and generalization of abstract logic, in this paper, we propose distilling logical rules to uniformly represent task knowledge sourced from distinct origins and facilitate deductive reasoning. We develop a collaborative annotating framework that iteratively integrates high-confidence predictions of rule-enhanced relation extractors with varying scales, efficiently obtaining reliable pseudo annotations from massive unlabeled samples without human supervision. Experiments under two inference settings show that our approach achieves new state-of-the-art performance on benchmark datasets in few-shot scenarios.</abstract>
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%0 Conference Proceedings
%T Improving Data Annotation for Low-Resource Relation Extraction with Logical Rule-Augmented Collaborative Language Models
%A Liu, Xiyang
%A Hu, Chunming
%A Zhang, Richong
%A Chen, Junfan
%A Xu, Baowen
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F liu-etal-2025-improving
%X Low-resource relation extraction aims to identify semantic relationships between entities using scarce labeled data. Recent studies exploit large language models to recognize relations based on retrieved examplars, yielding promising results. However, the reliability of predictions from these methods is constrained by the presence of irrelevant context within demonstrations and the inherent flaws of large language models in producing undesired outputs. Inspired by the precision and generalization of abstract logic, in this paper, we propose distilling logical rules to uniformly represent task knowledge sourced from distinct origins and facilitate deductive reasoning. We develop a collaborative annotating framework that iteratively integrates high-confidence predictions of rule-enhanced relation extractors with varying scales, efficiently obtaining reliable pseudo annotations from massive unlabeled samples without human supervision. Experiments under two inference settings show that our approach achieves new state-of-the-art performance on benchmark datasets in few-shot scenarios.
%R 10.18653/v1/2025.naacl-long.70
%U https://aclanthology.org/2025.naacl-long.70/
%U https://doi.org/10.18653/v1/2025.naacl-long.70
%P 1497-1510
Markdown (Informal)
[Improving Data Annotation for Low-Resource Relation Extraction with Logical Rule-Augmented Collaborative Language Models](https://aclanthology.org/2025.naacl-long.70/) (Liu et al., NAACL 2025)
ACL