Abstract: Relation extraction, as a key task in natural language processing, plays a significant role in deepening language understanding, constructing knowledge graphs, and optimizing information retrieval systems. However, traditional supervised learning methods are not well-suited for real-world scenarios due to the continuous emergence of new relations and the lack of large annotated datasets. Although the advent of large language models has significantly improved the performance of many natural language processing tasks, they still cannot effectively address the challenges of few-shot continual relation extraction. To fully leverage the semantic knowledge of large language models to mitigate catastrophic forgetting and overfitting issues, a novel few-shot continual relation extraction method, LAFA (large language model augmentation and feature alignment), is proposed. This method enhances representation alignment through various strategies such as relation instance rewriting, semantic expansion, and enhanced relation representation. It effectively improves the model adaptability to new relations and the retention of old knowledge while maintaining low data and computational costs. Experimental validation on two relation extraction datasets, FewRel and TACRED, demonstrates that LAFA outperforms existing methods in few-shot continual relation extraction tasks, particularly achieving the best results in incremental stages. Ablation experiments further reveal the significant contributions of each module to overall performance. Moreover, the inference efficiency and cost of LAFA are substantially lower than those of existing large language model-based methods, and it boasts strong scalability, being able to adapt to various language models.

Key words: large language model (LLM), relation extraction, continual learning, few-shot learning

摘要： 关系抽取作为自然语言处理的关键任务，对于深化语言理解、构建知识图谱以及优化信息检索系统具有重要作用。然而，由于新关系不断涌现且缺乏大量标注示例，传统的监督学习方法并不适合实际场景。尽管大语言模型的出现显著提升了许多自然语言处理任务的性能，但仍然无法直接有效地解决小样本持续关系抽取任务的挑战。为了充分利用大语言模型的语义知识来缓解灾难性遗忘与过拟合问题，提出了一种基于大语言模型增强表征对齐的小样本持续关系抽取方法LAFA，通过关系实例改写、语义扩充和关系增强表征等策略，在保持数据量和计算成本较低的同时，有效提升了模型对新关系的适应性和对旧知识的保持能力。在两个关系抽取数据集FewRel、TACRED上进行实验验证，与现有方法相比，LAFA在小样本持续关系抽取任务中展现出较好的效果，尤其在增量阶段取得了最佳的实验结果。通过消融实验进一步揭示了方法中各个模块对整体性能的显著贡献。LAFA的推理效率与开销远远低于现有的基于大语言模型的方法，并且具有很强的扩展性，能够适配多种语言模型。

关键词: 大语言模型（LLM）, 关系抽取, 持续学习, 小样本学习