{"type":"ScholarlyArticle","title":"Making LLMs as Fine-Grained Relation Extraction Data Augmentor","authors":[{"name":"Yifan Zheng","slug":"yifan-zheng","given":"Yifan","family":"Zheng","affiliation":"School of Computer Science and Engineering , Southeast University; Beijing Institute of Computer Technology and"},{"name":"Wenjun Ke","slug":"wenjun-ke","given":"Wenjun","family":"Ke","affiliation":"School of Computer Science and Engineering , Southeast University; Key Laboratory of New Generation Artifcial Intelligence Technology and Its Interdisciplinary Applications ( Southeast University"},{"name":"Qi Liu","slug":"qi-liu","given":"Qi","family":"Liu","affiliation":"Beijing Institute of Computer Technology and"},{"name":"Yuting Yang","slug":"yuting-yang","given":"Yuting","family":"Yang","affiliation":"Beijing Institute of Computer Technology and"},{"name":"Ruizhuo Zhao","slug":"ruizhuo-zhao","given":"Ruizhuo","family":"Zhao","affiliation":"Beijing Institute of Computer Technology and"},{"name":"Dacheng Feng","slug":"dacheng-feng","given":"Dacheng","family":"Feng","affiliation":"Beijing Institute of Computer Technology and"},{"name":"Jianwei Zhang","slug":"jianwei-zhang","given":"Jianwei","family":"Zhang","affiliation":"Beijing Institute of Computer Technology and; Laboratory for Big Data and Decision National University of Defense Technology zhengyifan"},{"name":"Zhi Fang","slug":"zhi-fang","given":"Zhi","family":"Fang","affiliation":"Beijing Institute of Computer Technology and; Laboratory for Big Data and Decision National University of Defense Technology zhengyifan"}],"abstract":"Relation Extraction (RE) identifies relations between entities in text, typically relying on supervised models that demand abundant high-quality data. Various approaches, including Data Augmentation (DA), have been proposed as promising solutions for addressing low-resource challenges in RE. However, existing DA methods in RE often struggle to ensure consistency and contextual diversity in generated data due to the fine-grained nature of RE. Inspired by the extensive generative capabilities of large language models (LLMs), we introduce a novel framework named ConsistRE, aiming to maintain context consistency in RE. ConsistRE initiates by collecting a substantial corpus from external resources and employing statistical algorithms and semantics to identify keyword hints closely related to relation instances. These keyword hints are subsequently integrated as contextual constraints in sentence generation, ensuring the preservation of relation dependence and diversity with LLMs. Additionally, we implement syntactic dependency selection to enhance the syntactic structure of the generated sentences. Experimental results from the evaluation of SemEval, TACRED, and TACREV datasets unequivocally demonstrate that ConsistRE outperforms other baselines in F1 values by 1.76%, 3.92%, and 2.53%, respectively, particularly when operating under low-resource experimental conditions.","doi":"10.24963/ijcai.2024/736","url":"https://ijcai.org/proceedings/2024/736","pdf":"https://ijcai.org/proceedings/2024/0736.pdf","markdown":"https://ijcai.org/proceedings/2024/736.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/736","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Natural Language Processing","track":"Main Track","number":736,"pages":{"first":6660,"last":6668},"keywords":["Natural Language Processing: NLP: Information retrieval and text mining","Natural Language Processing: NLP: Information extraction","Natural Language Processing: NLP: Language generation","Natural Language Processing: NLP: Resources and evaluation"],"videos":[],"references":[{"position":1,"raw":"[Alt et al., 2020] Christoph Alt, Aleksandra Gabryszak, and Leonhard Hennig. 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