CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmentation Generation

Yiruo Cheng, Mao, Ziliang Zhao, KABI, Hongjin Qian, Yongkang Wu, Tetsuya Sakai, Ji-Rong Wen, Zhicheng Dou

CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmentation Generation: 50 upvotes on Hugging Face Daily Papers, #1 of 11 papers on 2024-10-31. Day-by-day upvote history. It lost 7 votes when the Hub removed votes in bulk.

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.

Paper page on Hugging Face · arXiv

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