Abstract
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.
Abstract (translated)
信息抽取（IE）是一个将非结构化文本数据转换为结构化格式的变革性过程，通过采用实体和关系提取（RE）方法来完成。在信息抽取框架内，实体间关系的识别具有关键作用。尽管存在各种关系提取技术，但它们的有效性很大程度上依赖于访问标注数据和大量的计算资源。为了应对这些挑战，本文提出了一种基于已检索增强生成关系提取（RAG4RE）的方法，为提高关系抽取任务的性能提供了一条途径。 本文通过利用已有的基准数据集，如TACRED、TACREV、Re-TACRED和SemEval RE，全面评估了所提出RAG4RE方法的有效性。特别地，我们在研究中利用了重要的LLM，包括Flan T5、Llama2和Mistral。我们研究的结果表明，基于LLM的传统关系抽取方法在RAG4RE方法上显著超越了 performance，特别是在TACRED数据集及其变化中。此外，与以前的关系提取方法相比，我们在TACRED和TACREV数据集上的表现都有显著优势，这表明了RAG4RE方法在自然语言处理中的潜在推动作用。
URL
https://arxiv.org/abs/2404.13397
