@inproceedings{cheng-etal-2025-coral,
    title = "{CORAL}: Benchmarking Multi-turn Conversational Retrieval-Augmented Generation",
    author = "Cheng, Yiruo  and
      Mao, Kelong  and
      Zhao, Ziliang  and
      Dong, Guanting  and
      Qian, Hongjin  and
      Wu, Yongkang  and
      Sakai, Tetsuya  and
      Wen, Ji-Rong  and
      Dou, Zhicheng",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.findings-naacl.72/",
    pages = "1308--1330",
    ISBN = "979-8-89176-195-7",
    abstract = "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."
}