  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.
