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

Article


Siriwardhana, Shamane, Weerasekera, Rivindu, Wen, Elliott, Kaluarachchi, Tharindu, Rana, Rajib and Nanayakkara, Suranga. 2023. "Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering." Transactions of the Association for Computational Linguistics. 11, pp. 1-17. https://doi.org/10.1162/tacl_a_00530
Article Title

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

ERA Journal ID201665
Article CategoryArticle
AuthorsSiriwardhana, Shamane, Weerasekera, Rivindu, Wen, Elliott, Kaluarachchi, Tharindu, Rana, Rajib and Nanayakkara, Suranga
Journal TitleTransactions of the Association for Computational Linguistics
Journal Citation11, pp. 1-17
Number of Pages17
Year2023
PublisherThe MIT Press
Place of PublicationUnited States
ISSN2307-387X
Digital Object Identifier (DOI)https://doi.org/10.1162/tacl_a_00530
Web Address (URL)https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00530/114590/Improving-the-Domain-Adaptation-of-Retrieval
AbstractRetrieval 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 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 RAG-end2end to reconstruct a given sentence by accessing the relevant information from the external knowledge base. Our novel contribution is that, 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.
KeywordsRetrieval Augmented Generation; Open Domain; Domain Adaptation
ANZSRC Field of Research 2020460208. Natural language processing
Byline AffiliationsUniversity of Auckland, New Zealand
University of Southern Queensland
National University of Singapore
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