# Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering

**Authors:** Shamane Siriwardhana, Rivindu Weerasekera, Elliott Wen, Tharindu Kaluarachchi, Rajib Rana, Suranga Nanayakkara  
**arXiv:** [2210.02627](https://arxiv.org/abs/2210.02627)  
**Date:** 2022-10-06  
**Summary page:** https://www.summarizepaper.com/en/paper/2210.02627v1/

## Key points

- Study focuses on domain adaptation capabilities of the Retrieval Augment Generation (RAG) model in Open-Domain Question Answering (ODQA)
- Introduces RAG-end2end extension for joint training of retriever and generator components for adaptation to specialized domains like healthcare and news
- Leverages COVID-19, News, and Conversations datasets for domain adaptation through unique strategies and auxiliary signal incorporation
- Updates all components of external knowledge base during training to enhance adaptability
- Demonstrates improved performance across domains compared to original RAG model by enforcing sentence reconstruction based on relevant information from knowledge base
- Suggests future research directions in domain adaptation of RAG models, including Fact Checking and Summarization tasks
- Highlights importance of joint training of retriever and generator components in ODQA for effective domain adaptation
- Open-sourcing work through Huggingface Transformers library adds credibility and technical consistency to approach in advancing ODQA methodologies towards specialized domains

## Summary

In this study, we explore the domain adaptation capabilities of the Retrieval Augment Generation (RAG) model in Open-Domain Question Answering (ODQA). Our focus is on extending the use of RAG beyond Wikipedia-based knowledge bases to more specialized domains such as healthcare and news. To achieve this, we introduce \textit{RAG-end2end}, an extension that enables joint training of the retriever and generator components for adaptation to domain-specific knowledge bases. We leverage three distinct datasets - COVID-19, News, and Conversations - to facilitate domain adaptation through unique strategies for knowledge base generation and auxiliary signal incorporation. Our approach involves updating all components of the external knowledge base during training to enhance adaptability. By introducing an auxiliary training signal that enforces sentence reconstruction based on relevant information from the knowledge base, \textit{RAG-end2end} demonstrates improved performance across all three domains compared to the original RAG model. Our findings suggest potential directions for future research in domain adaptation of RAG models, including exploring its use in tasks such as Fact Checking and Summarization. Additionally, enhancing the retriever component within the RAG architecture and exploring alternative auxiliary signals could further improve overall model performance. Overall, our study highlights the importance of joint training of retriever and generator components in ODQA for effective domain adaptation. The open-sourcing of our work through the Huggingface Transformers library adds credibility and technical consistency to our approach in advancing ODQA methodologies towards more specialized domains.