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Retrieval Augmentation Reduces Hallucination in Conversation

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "arXiv — non-exclusive licence to distribute" does not allow redistributing the text itself. Original — arxiv.org/pdf/2104.07567

Summary

Large dialogue models sound fluent but often state false facts, a failure known as knowledge hallucination. The authors at Facebook AI Research adapt neural-retrieval-in-the-loop methods, previously successful in open-domain QA, to knowledge-grounded conversation. This setting is harder than QA because the query is a long multi-turn context and the reply must stay coherent and engaging. They study combinations of retrievers, rankers and encoder-decoders built on RAG and Fusion-in-Decoder (FiD). Their additions are:

  • Poly-encoder re-ranking of documents
  • iterative retrieval
  • FiD with end-to-end-trained retrievers
  • RAG-Turn, which retrieves separately for each dialogue turn

They retrieve over Wikipedia and evaluate on Wizard of Wikipedia and CMU_DoG, where the best systems set new state-of-the-art results. Human judges found that hallucinated replies dropped by more than 60% compared with non-retrieval models, while conversational quality held up. Gains were larger on unseen topics. This suggests that external retrieval can supply knowledge a model's parameters lack, offering a practical route to more truthful chatbots.

Key points

  • Datasets: Wizard of Wikipedia (Seen/Unseen splits) and CMU_DoG. Variants compared include RAG-Sequence, RAG-Token, RAG-Turn (DTT, DO), FiD-RAG and DPR-Poly, mostly with a BART generator.
  • Knowledgeability metrics improved over the baseline by about 70% on in-distribution data and 85% on out-of-distribution data.
  • Retrieving more documents in RAG-Token and FiD-RAG improves perplexity and F1 but lowers Knowledge F1, and human ratings indicate more hallucination. The authors settle on 25 re-ranked documents.
  • Restricting the knowledge source to a Wikipedia subset of roughly 3,000 articles covering WoW topics makes retrieval easier and raises scores. Extra pre-training of the retriever or re-ranker brings little benefit.
  • The models and training code are released via ParlAI (parl.ai/projects/hallucination).

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: Other

Cited by

378 papers of our library cite it (of the 140673 whose bibliographies we read); 27 of them have a page here, 351 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2104.07567 · arXiv 2104.07567

@misc{shuster2021retrieval,
  title = {{Retrieval Augmentation Reduces Hallucination in Conversation}},
  author = {Shuster, Kurt and Poff, Spencer and Chen, Moya and Kiela, Douwe and Weston, Jason},
  year = {2021},
  doi = {10.48550/arxiv.2104.07567},
  eprint = {2104.07567},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2104.07567}
}

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