ariya2357/CORAL

CORAL是一个大规模的多轮对话检索增强生成（RAG）基准，旨在系统评估和推进对话RAG系统。该数据集评估了三个关键任务：（1）对话段落检索：评估系统基于多轮上下文从大量文档集中检索相关信息的能力；（2）响应生成：评估系统生成准确且上下文丰富的答案的能力；（3）引用标注：确保生成的响应透明且有据可依，要求正确标注来源。

CORAL is a large-scale multi-turn conversational RAG benchmark that fulfills the critical features mentioned in our paper to systematically evaluate and advance conversational RAG systems. The dataset evaluates conversational RAG systems across three essential tasks: (1) Conversational Passage Retrieval, assessing the system’s ability to retrieve the relevant information from a large document set based on multi-turn context; (2) Response Generation, evaluating the system’s capacity to generate accurate, contextually rich answers; (3) Citation Labeling, ensuring that the generated responses are transparent and grounded by requiring correct attribution of sources.