CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmentation Generation
This addresses the gap in evaluating RAG systems for real-world multi-turn conversations, though it is incremental as it focuses on benchmarking rather than novel method development.
The authors tackled the lack of benchmarks for multi-turn conversational retrieval-augmented generation (RAG) by introducing CORAL, a large-scale benchmark derived from Wikipedia, which revealed significant opportunities for improvement in existing methods.
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.