التفاصيل البيبلوغرافية
| العنوان: |
CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmentation Generation |
| المؤلفون: |
Cheng, Yiruo, Mao, Kelong, Zhao, Ziliang, Dong, Guanting, Qian, Hongjin, Wu, Yongkang, Sakai, Tetsuya, Wen, Ji-Rong, Dou, Zhicheng |
| سنة النشر: |
2024 |
| المجموعة: |
Computer Science |
| مصطلحات موضوعية: |
Computer Science - Information Retrieval, Computer Science - Computation and Language |
| الوصف: |
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. |
| نوع الوثيقة: |
Working Paper |
| URL الوصول: |
http://arxiv.org/abs/2410.23090 |
| رقم الانضمام: |
edsarx.2410.23090 |
| قاعدة البيانات: |
arXiv |