mlsanityData to 14 Oct 2026

1-PAGER: One Pass Answer Generation and Evidence Retrieval

arXiv 2310.16568 ↗First posted 25 Oct 2023cs.CLcited by 7

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Abstract (arXiv)

We present 1-Pager the first system that answers a question and retrieves evidence using a single Transformer-based model and decoding process. 1-Pager incrementally partitions the retrieval corpus using constrained decoding to select a document and answer string, and we show that this is competitive with comparable retrieve-and-read alternatives according to both retrieval and answer accuracy metrics. 1-Pager also outperforms the equivalent closed-book question answering model, by grounding predictions in an evidence corpus. While 1-Pager is not yet on-par with more expensive systems that read many more documents before generating an answer, we argue that it provides an important step toward attributed generation by folding retrieval into the sequence-to-sequence paradigm that is currently dominant in NLP. We also show that the search paths used to partition the corpus are easy to read and understand, paving a way forward for interpretable neural retrieval.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 27 of its 46 references that are in the map. First posted on arXiv 25 Oct 2023.

Builds on

27 of 46 references.

References · 27

  1. 2017Attention Is All You Needcited by 60,361
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  3. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  4. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  5. 2020ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERTcited by 1,178
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  12. 2021ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interactioncited by 476
15 more
  1. 2020Autoregressive Entity Retrievalcited by 334
  2. 2022Generate rather than Retrieve: Large Language Models are Strong Context Generatorscited by 298
  3. 2022Teaching language models to support answers with verified quotescited by 292
  4. 2021Measuring Attribution in Natural Language Generation Modelscited by 256
  5. 2022Can language models learn from explanations in context?cited by 249
  6. 2022Transformer Memory as a Differentiable Search Indexcited by 247
  7. 2021PAQ: 65 Million Probably-Asked Questions and What You Can Do With Themcited by 200
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  10. 2022Autoregressive Search Engines: Generating Substrings as Document Identifierscited by 113
  11. 2021Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representationscited by 60
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  15. 2021Boosting Search Engines with Interactive Agentscited by 19

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 1; 2026: 1.

7 papers.

  1. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  2. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  3. 2023A Survey of Large Language Models Attributioncited by 71
  4. 2024From RAG to RICHES: Retrieval Interlaced with Sequence Generationcited by 5
  5. 2024Citekit: A Modular Toolkit for Large Language Model Citation Generationcited by 5
  6. 2025Equipping Retrieval-Augmented Large Language Models with Document Structure Awarenesscited by 1
  7. 2026Attribution Techniques for Mitigating Hallucinated Information in RAG Systems: A Surveycited by 0