Related papers: DuetRAG: Collaborative Retrieval-Augmented Generation

DuetRAG: Collaborative Retrieval-Augmented Generation

URL: http://arxiv.org/abs/2405.13002v1
Date: Sun, 12 May 2024 09:48:28 GMT
Title: DuetRAG: Collaborative Retrieval-Augmented Generation
Authors: Dian Jiao, Li Cai, Jingsheng Huang, Wenqiao Zhang, Siliang Tang, Yueting Zhuang,
Abstract summary: Collaborative Retrieval-Augmented Generation framework, DuetRAG, proposed. bootstrapping philosophy is to simultaneously integrate the domain fintuning and RAG models.
Score: 57.440772556318926
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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