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Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Paper · 2020 · Patrick Lewis, Ethan Perez, Aleksandra Piktus et al.

Summary

Large pre-trained language models store many facts in their weights, but they offer poor means of precisely accessing this knowledge, updating it, or showing where an answer came from. In addition, they are prone to hallucination. The authors propose RAG, a general-purpose fine-tuning approach. It combines the generative seq2seq model BART as a "parametric memory" with an external "non-parametric" memory. This memory is a dense vector index of Wikipedia, accessed by the neural retriever DPR. The model treats the retrieved passages as a latent variable and marginalizes over them. The retriever and the generator are trained jointly, and the model receives no direct supervision on which document is correct. The authors compare two variants: RAG-Sequence uses a single document for the entire answer, whereas RAG-Token can draw on a different document for each token. The system set the then state of the art on three open-domain QA tasks. In generation tasks, its outputs proved more specific, more diverse, and more factually accurate than those of BART without retrieval. Moreover, the model's knowledge can be updated simply by replacing the index, without retraining.

Key points

  • Components: the BART-large generator (~400M parameters) and the DPR retriever (a bi-encoder based on two BERT-BASE models). The most relevant documents are retrieved via MIPS using FAISS with the HNSW approximation.
  • The knowledge base is a December 2018 Wikipedia dump split into 100-word chunks, roughly 21M documents in total. During training, 5 or 10 documents are retrieved per query.
  • During training, the document encoder and the index are kept fixed. Only the query encoder BERT-q and BART are fine-tuned, so the index does not need to be periodically rebuilt, as in REALM.
  • The best results at the time were achieved on Natural Questions, WebQuestions, and CuratedTrec, and on TriviaQA the model clearly outperformed approaches with specialized pre-training. On FEVER it fell short of the best pipeline systems by only 4.3%, even though those had direct retrieval supervision.
  • The code is open-sourced in the HuggingFace Transformers library. Two decoding procedures are proposed for RAG-Sequence: the more accurate "Thorough Decoding" and the faster "Fast Decoding".

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

 Institutions

🏢MetaMenlo Park · United States183 works · 228,558 citations🏢Meta (United Kingdom)London · United Kingdom46 works · 18,033 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksSocial Sciences · 1,772 worksSafety Research · 543 worksEngineering · 133 worksSafety, Risk, Reliability and Quality · 10 works

🧭 Topics 1Research subjects and languages the work is about.Information retrieval & search · 473 works

💡 Concepts 3Key ideas and methods explained on our concept pages.Natural language processing · 131 worksQuestion answering · 53 worksRetrieval-augmented generation · 50 works

Cite

Cite as Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel and Douwe Kiela. 2020. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. arXiv:2005.11401.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2005.11401 · arXiv 2005.11401

@misc{lewis2020retrieval,
  title = {{Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks}},
  author = {Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel and Douwe Kiela},
  year = {2020},
  doi = {10.48550/arxiv.2005.11401},
  eprint = {2005.11401},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2005.11401}
}

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