DuetRAG：协作式检索增强生成
DuetRAG: Collaborative Retrieval-Augmented Generation
- Zhejiang University（浙江大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对复杂领域问题中RAG检索不相关导致生成质量低的问题，提出DuetRAG框架，通过同时整合领域微调和RAG模型提升检索质量，并在HotPot QA上达到专家水平。
AI中文摘要：
检索增强生成（RAG）方法通过将相关检索段落增强到大型语言模型（LLMs）的输入中，减少了知识密集型任务中的事实错误。然而，当代RAG方法在处理复杂领域问题（如HotPot QA）时，由于缺乏相应的领域知识，常遭遇不相关知识检索的问题，导致生成质量低下。为解决此问题，我们提出了一种新颖的协作式检索增强生成框架——DuetRAG。我们的自举理念是同时整合领域微调和RAG模型，以提高知识检索质量，从而增强生成质量。最后，我们展示了DuetRAG在HotPot QA上与专家人类研究者的匹配表现。
英文摘要：
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.
