Retrieval Augment Generation (RAG) is a recent advancement in Open-Domain Question Answering (ODQA). RAG has only been trained and explored with a Wikipedia-based external knowledge base and is not optimized for use in other specialized domains such as healthcare and news. In this paper, we evaluate the impact of joint training of the retriever and generator components of RAG for the task of domain adaptation in ODQA. We propose \textit{RAG-end2end}, an extension to RAG, that can adapt to a domain-specific knowledge base by updating all components of the external knowledge base during training. In addition, we introduce an auxiliary training signal to inject more domain-specific knowledge. This auxiliary signal forces \textit{RAG-end2end} to reconstruct a given sentence by accessing the relevant information from the external knowledge base. Our novel contribution is unlike RAG, RAG-end2end does joint training of the retriever and generator for the end QA task and domain adaptation. We evaluate our approach with datasets from three domains: COVID-19, News, and Conversations, and achieve significant performance improvements compared to the original RAG model. Our work has been open-sourced through the Huggingface Transformers library, attesting to our work's credibility and technical consistency.

本论文旨在研究将 RAG 用于领域适应的可行性，提出了一种名为 RAG-end2end 的新模型，相较于 RAG 模型，它通过联合训练 RAG 组件以及外部知识库的更新，实现了针对特定领域的知识库自适应以及更准确的问答任务。在 COVID-19、新闻和对话等三个领域上的测试表明，该新模型相较于原始 RAG 模型在数据集中的表现有明显提升。

提升检索增强生成模型（RAG）在开放领域问答中的领域自适应能力