Retrieval-Augmented Generation (RAG) has become a powerful paradigm for enhancing large language models (LLMs) through external knowledge retrieval. Despite its widespread attention, existing academic research predominantly focuses on single-turn RAG, leaving a significant gap in addressing the complexities of multi-turn conversations found in real-world applications. To bridge this gap, we introduce CORAL, a large-scale benchmark designed to assess RAG systems in realistic multi-turn conversational settings. CORAL includes diverse information-seeking conversations automatically derived from Wikipedia and tackles key challenges such as open-domain coverage, knowledge intensity, free-form responses, and topic shifts. It supports three core tasks of conversational RAG: passage retrieval, response generation, and citation labeling. We propose a unified framework to standardize various conversational RAG methods and conduct a comprehensive evaluation of these methods on CORAL, demonstrating substantial opportunities for improving existing approaches.
@article{arxiv.2410.23090,
title = {CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmentation Generation},
author = {Yiruo Cheng and Kelong Mao and Ziliang Zhao and Guanting Dong and Hongjin Qian and Yongkang Wu and Tetsuya Sakai and Ji-Rong Wen and Zhicheng Dou},
journal= {arXiv preprint arXiv:2410.23090},
year = {2024}
}