Information extraction (IE) is a transformative process that converts unstructured text data into a structured format by employing entity and relation extraction (RE) methodologies. Identifying the relation between a pair of entities plays a crucial role within this framework. Despite the availability of various techniques for RE, their efficacy heavily depends on access to labeled data and substantial computational resources. To address these challenges, large language models (LLMs) have emerged as promising solutions; however, they are prone to generating hallucinated responses due to the limitations of their training data. To overcome these shortcomings, this work proposes a retrieval-augmented generation-based relation extraction (RAG4RE) approach to enhance RE performance. We evaluate the effectiveness of RAG4RE using various LLMs. By leveraging established benchmarks such as TACRED, TACREV, Re-TACRED and SemEval RE datasets, we aim to comprehensively assess the efficacy of our methodology. Specifically, we employ prominent LLMs, including Flan T5, Llama2, and Mistral, in our investigation. The results of our work demonstrate that RAG4RE outperforms traditional RE methods based solely on LLMs, with significant improvements observed 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.
