基于检索增强生成的关系抽取
Retrieval-Augmented Generation-based Relation Extraction
AI总结：
针对传统关系抽取依赖标注数据与计算资源、大语言模型易产生幻觉的问题，提出RAG4RE方法，经多基准多模型验证，性能优于传统及现有关系抽取方法。
摘要：
信息抽取（IE）是一种通过实体与关系抽取（RE）方法将非结构化文本数据转换为结构化格式的变革性流程。在该框架中，识别实体对之间的关系发挥着关键作用。尽管现有多种关系抽取技术，但它们的效果高度依赖标注数据的获取与大量计算资源。为应对这些挑战，大语言模型（LLM）成为颇具前景的解决方案；然而，它们受自身训练数据影响可能返回幻觉性响应。为克服这些局限，本文提出了基于检索增强生成的关系抽取方法（Retrieved-Augmented Generation-based Relation Extraction，RAG4RE），为提升关系抽取任务性能提供了路径。\n本研究采用不同大语言模型评估了RAG4RE方法的有效性。通过使用TACRED、TACREV、Re-TACRED和SemEval RE数据集等成熟基准，我们旨在全面评估RAG4RE方法的效果。具体而言，我们在研究中使用了Flan T5、Llama2和Mistral等主流大语言模型。研究结果表明，RAG4RE方法的性能优于仅基于大语言模型的传统关系抽取方法，这一点在TACRED数据集及其变体上表现尤为明显。此外，在TACRED和TACREV数据集上，我们的方法相较于以往的关系抽取方法均展现出优异性能，凸显了其在推动自然语言处理领域关系抽取任务发展方面的有效性与潜力。
英文摘要：
Information Extraction (IE) is a transformative process that converts unstructured text data into a structured format by employing entity and relation extraction (RE) methodologies. The identification of the relation between a pair of entities plays a crucial role within this framework. Despite the existence of various techniques for relation extraction, their efficacy heavily relies on access to labeled data and substantial computational resources. In addressing these challenges, Large Language Models (LLMs) emerge as promising solutions; however, they might return hallucinating responses due to their own training data. To overcome these limitations, Retrieved-Augmented Generation-based Relation Extraction (RAG4RE) in this work is proposed, offering a pathway to enhance the performance of relation extraction tasks. This work evaluated the effectiveness of our RAG4RE approach utilizing different LLMs. Through the utilization of established benchmarks, such as TACRED, TACREV, Re-TACRED, and SemEval RE datasets, our aim is to comprehensively evaluate the efficacy of our RAG4RE approach. In particularly, we leverage prominent LLMs including Flan T5, Llama2, and Mistral in our investigation. The results of our study demonstrate that our RAG4RE approach surpasses performance of traditional RE approaches based solely on LLMs, particularly evident in the TACRED dataset and its variations. Furthermore, our approach exhibits remarkable performance compared to previous RE methodologies across both TACRED and TACREV datasets, underscoring its efficacy and potential for advancing RE tasks in natural language processing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
