2 citations · 5 across the 8 of their papers we have counts for
4 papers · 2 filters
PrefRAG: Preference-Driven Multi-Source Retrieval Augmented Generation
Qingfei Zhao, Ruobing Wang, Yukuo Cen +3
Retrieval-Augmented Generation (RAG) has emerged as a reliable external knowledge augmentation technique to mitigate hallucination issues and parameterized knowledge limitations in…
LongRAG: A Dual-Perspective Retrieval-Augmented Generation Paradigm for Long-Context Question Answering
Qingfei Zhao, Ruobing Wang, Yukuo Cen +4
Long-Context Question Answering (LCQA), a challenging task, aims to reason over long-context documents to yield accurate answers to questions. Existing long-context Large Language…
DeepNote: Note-Centric Deep Retrieval-Augmented Generation
Ruobing Wang, Qingfei Zhao, Yukun Yan +9
Retrieval-Augmented Generation (RAG) mitigates factual errors and hallucinations in Large Language Models (LLMs) for question-answering (QA) by incorporating external knowledge. Ho…
RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework
Kunlun Zhu, Yifan Luo, Dingling Xu +10
Retrieval-Augmented Generation (RAG) is a powerful approach that enables large language models (LLMs) to incorporate external knowledge. However, evaluating the effectiveness of RA…