CACRE: A Weakly Supervised Method for Cross-Attention Contrastive Relation Extraction With Large Language Models
文献类型：期刊论文
| 作者 | Hu, Zhikui1; Zi, Kangli2; Luo, Tianyu2; Huang, Yuwei2; Wang, Shi2 |
| 刊名 | IEEE ACCESS
 |
| 出版日期 | 2026 |
| 卷号 | 14页码:18718-18729 |
| 关键词 | Semantics Contrastive learning Feature extraction Weak supervision Vectors Training Large language models Data mining Tensors Reliability Relation extraction weak supervision contrastive learning LLMs |
| ISSN号 | 2169-3536 |
| DOI | 10.1109/ACCESS.2026.3660343 |
| 英文摘要 | In the relation extraction (RE) task, large language models (LLMs) have shown remarkable capabilities in predicting unknown relations, offering significant improvements in efficiency and flexibility over traditional methods. However, the probabilistic nature of the generation process in LLMs may lead to the occurrence of hallucinations, causing inaccurate relation triples to be generated. To mitigate this problem, this paper proposes a novel weakly supervised method, Cross-Attention Contrastive Relation Extraction (CACRE), which aims at detecting erroneous relation triples generated by LLMs and then effectively distinguishing valid ones. The CACRE leverages contrastive learning and cross-attention mechanisms. Specifically, contrastive learning is applied to distinguish between positive and negative relation triples, enhancing the model's feature extraction capability by learning discriminative features. Subsequently, a cross-attention mechanism is employed to capture the semantic associations between texts and triples, thereby improving the model's ability to understand and extract information from the input content. Experiment results on the DuIE2.0 dataset and the TACRED dataset demonstrate that CACRE significantly outperforms baseline LLMs, with average improvements of 12% and 8% in precision, respectively. |
| 资助项目 | National Key Research and Development Program of China[2024QY210004] ; National Key Research and Development Program of China[2022YFC3302300] |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:001687453200010 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/42797]  |
| 专题 | 中国科学院计算技术研究所 |
| 通讯作者 | Zi, Kangli |
| 作者单位 | 1.Jiangsu Univ Sci & Technol, Zhenjiang 212100, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, State Key Lab AI Safety, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Hu, Zhikui,Zi, Kangli,Luo, Tianyu,et al. CACRE: A Weakly Supervised Method for Cross-Attention Contrastive Relation Extraction With Large Language Models[J]. IEEE ACCESS,2026,14:18718-18729. |
| APA | Hu, Zhikui,Zi, Kangli,Luo, Tianyu,Huang, Yuwei,&Wang, Shi.(2026).CACRE: A Weakly Supervised Method for Cross-Attention Contrastive Relation Extraction With Large Language Models.IEEE ACCESS,14,18718-18729. |
| MLA | Hu, Zhikui,et al."CACRE: A Weakly Supervised Method for Cross-Attention Contrastive Relation Extraction With Large Language Models".IEEE ACCESS 14(2026):18718-18729. |
入库方式： OAI收割
来源：计算技术研究所
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