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 ID | 201665 |
| Article Category | Article |
| Authors | Siriwardhana, Shamane, Weerasekera, Rivindu, Wen, Elliott, Kaluarachchi, Tharindu, Rana, Rajib and Nanayakkara, Suranga |
| Journal Title | Transactions of the Association for Computational Linguistics |
| Journal Citation | 11, pp. 1-17 |
| Number of Pages | 17 |
| Year | 2023 |
| Publisher | The MIT Press |
| Place of Publication | United States |
| ISSN | 2307-387X |
| 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 |
| Abstract | 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 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. |
| Keywords | Retrieval Augmented Generation; Open Domain; Domain Adaptation |
| ANZSRC Field of Research 2020 | 460208. Natural language processing |
| Byline Affiliations | University of Auckland, New Zealand |
| University of Southern Queensland | |
| National University of Singapore |
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https://research.usq.edu.au/item/z26x4/improving-the-domain-adaptation-of-retrieval-augmented-generation-rag-models-for-open-domain-question-answering
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