  Retrieval-Augmented Generation (RAG) methods augment the input of Large
Language Models (LLMs) with relevant retrieved passages, reducing factual
errors in knowledge-intensive tasks. However, contemporary RAG approaches
suffer from irrelevant knowledge retrieval issues in complex domain questions
(e.g., HotPot QA) due to the lack of corresponding domain knowledge, leading to
low-quality generations. To address this issue, we propose a novel
Collaborative Retrieval-Augmented Generation framework, DuetRAG. Our
bootstrapping philosophy is to simultaneously integrate the domain fintuning
and RAG models to improve the knowledge retrieval quality, thereby enhancing
generation quality. Finally, we demonstrate DuetRAG' s matches with expert
human researchers on HotPot QA.
