Lab leader

Patrick Lewis

Also known as Patrick S H Lewis

Patrick Lewis works on question answering and retrieval-augmented generation, which connects language models with external information. Early research examined factual knowledge in pretrained models and overlap in question-answering benchmarks. RAG, PAQ’s 65 million generated question-answer pairs, and Atlas for few-shot knowledge tasks show the scope of his research. His EACL 2021 Best Short Paper award recognized the benchmark-overlap study, and his UCL PhD focused on neural question answering with retrieval across UCL and FAIR. As of October 2026, he directs Agentic AI at Cohere, leading RAG, tool-use, and agent teams.

Now at
Cohere
Works
45

Publication numbers from OpenAlex, as of 14 October 2026. Citations and h-index are hidden because this OpenAlex record may be incomplete or mix in other authors.

Photo of Patrick Lewis

Timeline

  1. 2019

    Testing what language models know

    With six coauthors, he studies whether pretrained language models hold factual relations and answer open-domain questions.

  2. 2020

    Retrieval joins generation

    He coauthors the RAG model, which pairs a sequence-to-sequence language generator with a dense Wikipedia index accessed by a neural retriever. The paper reports state-of-the-art results on three open-domain question-answering tasks.

  3. 2021

    An award for benchmark work

    With Pontus Stenetorp and Sebastian Riedel, he studies overlap in open-domain question-answering benchmarks. Their paper receives the EACL 2021 Best Short Paper Award.

  4. 2021

    Questions at scale

    His PAQ paper introduces 65 million automatically generated question-answer pairs and RePAQ, a related QA-pair retriever. His 2021 doctoral thesis focuses on neural question answering through retrieval and generation; he completes his PhD while splitting time between FAIR and UCL.

  5. 2022

    Learning from few examples

    With Gautier Izacard and eight coauthors, he presents Atlas, a retrieval-augmented model for knowledge-intensive tasks that can learn from very few examples. On Natural Questions, it exceeds 42% accuracy with 64 examples.

  6. As of October 2026

    Directing agentic AI

    He is Director of Agentic AI at Cohere, where he leads RAG, tool-use, and agent teams.

Sources and credits

Photo: patricklewis.io; Patrick Lewis; creator not stated. License: Not stated by source. Source: patricklewis.io. Cropped to a square and resized. Ask us to remove or replace this photo.

  1. Language Models as Knowledge Bases? - ACL Anthology aclanthology.org. Checked 14 October 2026.
  2. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks | UCL NLP nlp.cs.ucl.ac.uk. Checked 14 October 2026.
  3. Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets - ACL Anthology aclanthology.org. Checked 14 October 2026.
  4. Best Paper Awards - EACL 2021 2021.eacl.org. Checked 14 October 2026.
  5. PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them | UCL NLP nlp.cs.ucl.ac.uk. Checked 14 October 2026.
  6. Patrick Lewis patricklewis.io. Checked 14 October 2026.
  7. Publications | Patrick Lewis patricklewis.io. Checked 14 October 2026.
  8. Atlas: Few-shot Learning with Retrieval Augmented Language Models arxiv.org. Checked 14 October 2026.
  9. Japanese Symposium on Open LLM 登壇者情報 | 国立情報学研究所 大規模言語モデル研究開発センター llmc.nii.ac.jp. Checked 14 October 2026.
  10. UCL NLP nlp.cs.ucl.ac.uk. Checked 14 October 2026.