Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering (2023.tacl-1)
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Shamane Siriwardhana, Rivindu Weerasekera, Elliott Wen, Tharindu Kaluarachchi, Rajib Rana, Suranga Nanayakkara
| Challenge: | Retrieval Augment Generation (RAG) has been developed for use in open-domain question answering (ODQA) but it is not optimized for use with other specialized domains such as healthcare and news. |
| Approach: | They propose an extension to RAG that can adapt to a domain-specific knowledge base by updating all components of the external knowledge base during training. |
| Outcome: | The proposed extension can adapt to a domain-specific knowledge base by updating all components of the external knowledge base during training. |
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