---
title: "Improving the Domain Adaptation of — Siriwardhana 2023"
description: "RAG combines a DPR retriever with a BART generator and an external knowledge base."
authors: ["Shamane Siriwardhana", "Rivindu Weerasekera", "Elliott Wen", "Tharindu Kaluarachchi", "Rajib Rana", "Suranga Nanayakkara"]
year: 2023
venue: "Transactions of the Association for Computational Linguistics"
source: "https://aclanthology.org/2023.tacl-1.1/"
doi: "10.1162/tacl_a_00530"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/siriwardhana-2023-improving-domain-adaptation-retrieval-augmented/"
---
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# 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 · **Year:** 2023 · **Venue:** Transactions of the Association for Computational Linguistics · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2023.tacl-1.1/) · [PDF](https://aclanthology.org/2023.tacl-1.1.pdf) · [DOI 10.1162/tacl\_a\_00530](https://doi.org/10.1162/tacl_a_00530) · [OpenAlex](https://openalex.org/W4317898419)

**Citations:** 323 citations (openalex-snapshot · 2026-10-14) · [80](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
RAG combines a DPR retriever with a BART generator and an external knowledge base. It was developed and tested only with Wikipedia, and its passage encodings and index stay frozen during fine-tuning. This makes its transfer to specialized fields uncertain. The authors present RAG-end2end, which jointly trains the question encoder, the passage encoder and BART on the downstream QA task. Meanwhile, separate asynchronous processes repeatedly re-embed the domain knowledge base and rebuild its FAISS index. They also add an auxiliary objective in which the model regenerates a statement about a document from passages it retrieves itself. A control token tells the reader whether it is answering or reconstructing. Experiments cover three domains: COVID-19 research, News and Conversations. RAG-end2end outperforms the original RAG in all of them, and the reconstruction signal improves both retrieval and answer quality. The main lesson is that retriever adaptation drives domain transfer. Refreshing only the question encoder while keeping old passage embeddings can even lower performance. The implementation is released in HuggingFace Transformers.

## Key points
- **Architecture:** every RAG component is updated during training, including DPR, BART and the knowledge-base embeddings and index. The cost grows with knowledge-base size, since re-encoding needs dedicated GPUs and FAISS re-indexing depends on CPU cores.
- **Evaluation domains:** the three test areas are News (Trischler et al., 2016), Conversations (Wu et al., 2021b) and research on COVID-19 (Wang et al., 2020).
- **Retriever training:** training DPR inside RAG-end2end works better than fine-tuning DPR separately. It needs only QA pairs, so no gold passages or synthetic questions are required. Even RAG-original gains when it starts from a domain-adapted DPR checkpoint.
- **In-domain check on SQuAD:** SQuAD was recast as ODQA with 30K passages of 100 words each. RAG-end2end raised EM from 28.12 to 40.02 and F1 from 39.42 to 52.63. Its two retrieval scores rose from 59.64 to 75.79 and from 72.38 to 85.57.
- **Future directions:** the authors propose applying RAG-end2end to fact checking, summarization and dialogue. They also suggest studying factual consistency and hallucination, and testing other auxiliary signals.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

80 papers of our library cite it (of the 140884 whose bibliographies we read); 5 of them have a page here, 75 not yet.

- [Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models](https://md.research.elamur.ai/free/en/papers/dahl-2024-large-legal-fictions-profiling-legal/)
- [A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models](https://md.research.elamur.ai/free/en/papers/fan-2024-survey-rag-meeting-llms-towards/)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/gao-2023-retrieval-augmented-generation-large-language/)
- [Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools](https://md.research.elamur.ai/free/en/papers/magesh-2024-hallucination-free-assessing-reliability-leading/)
- [RBR: RAG-Based Open-Domain Question Answering Using a Ranking Approach to Document Retrieval](https://md.research.elamur.ai/free/en/papers/naravajhula-2026-rbr-rag-based-open-domain/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@article{siriwardhana2023improving,
  title = {{Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering}},
  author = {Siriwardhana, Shamane and Weerasekera, Rivindu and Wen, Elliott and Kaluarachchi, Tharindu and Rana, Rajib and Nanayakkara, Suranga},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2023},
  doi = {10.1162/tacl\_a\_00530},
  url = {https://doi.org/10.1162/tacl_a_00530}
}
```

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