| Retrieval-augmented generation for knowledge-intensive nlp tasks P Lewis, E Perez, A Piktus, F Petroni, V Karpukhin, N Goyal, H Küttler, ... NeurIPS 2020, 2020 | 6671 | 2020 |
| Dense passage retrieval for open-domain question answering V Karpukhin, B Oğuz, S Min, P Lewis, L Wu, S Edunov, D Chen, W Yih EMNLP 2020, 2020 | 3807 | 2020 |
| Language models as knowledge bases? F Petroni, T Rocktäschel, P Lewis, A Bakhtin, Y Wu, AH Miller, S Riedel EMNLP 2019, 2019 | 3038 | 2019 |
| Few-shot learning with retrieval augmented language models G Izacard, P Lewis, M Lomeli, L Hosseini, F Petroni, T Schick, ... arXiv preprint arXiv:2208.03299 1 (2), 4, 2022 | 718* | 2022 |
| KILT: a benchmark for knowledge intensive language tasks F Petroni, A Piktus, A Fan, P Lewis, M Yazdani, N De Cao, J Thorne, ... NAACL 2021, 2020 | 541 | 2020 |
| Mlqa: Evaluating cross-lingual extractive question answering P Lewis, B Oğuz, R Rinott, S Riedel, H Schwenk ACL 2020, 2019 | 507 | 2019 |
| Pretrained language models for biomedical and clinical tasks: understanding and extending the state-of-the-art P Lewis, M Ott, J Du, V Stoyanov Proceedings of the 3rd clinical natural language processing workshop, 146-157, 2020 | 256 | 2020 |
| How Context Affects Language Models' Factual Predictions F Petroni, P Lewis, A Piktus, T Rocktäschel, Y Wu, AH Miller, S Riedel AKBC 2020, 2020 | 254 | 2020 |
| Paq: 65 million probably-asked questions and what you can do with them P Lewis, Y Wu, L Liu, P Minervini, H Küttler, A Piktus, P Stenetorp, ... TACL 2021, 2021 | 217 | 2021 |
| Question and answer test-train overlap in open-domain question answering datasets P Lewis, P Stenetorp, S Riedel EACL 2021, 2020 | 204 | 2020 |
| Unsupervised question answering by cloze translation P Lewis, L Denoyer, S Riedel ACL 2019, 2019 | 185 | 2019 |
| Interpretation of natural language rules in conversational machine reading M Saeidi, M Bartolo, P Lewis, S Singh, T Rocktäschel, M Sheldon, ... EMNLP 2018, 2018 | 184 | 2018 |
| Unsupervised question decomposition for question answering E Perez, P Lewis, W Yih, K Cho, D Kiela EMNLP 2020, 2020 | 177 | 2020 |
| Autoregressive search engines: Generating substrings as document identifiers M Bevilacqua, G Ottaviano, P Lewis, W Yih, S Riedel, F Petroni NeurIPS 2022, 2022 | 171 | 2022 |
| Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval W Xiong, XL Li, S Iyer, J Du, P Lewis, WY Wang, Y Mehdad, W Yih, ... ICLR 2021, 2020 | 157* | 2020 |
| Snapkv: Llm knows what you are looking for before generation Y Li, Y Huang, B Yang, B Venkitesh, A Locatelli, H Ye, T Cai, P Lewis, ... Advances in Neural Information Processing Systems 37, 22947-22970, 2024 | 125 | 2024 |
| Peer: A collaborative language model T Schick, J Dwivedi-Yu, Z Jiang, F Petroni, P Lewis, G Izacard, Q You, ... arXiv preprint arXiv:2208.11663, 2022 | 125 | 2022 |
| Task-aware retrieval with instructions A Asai, T Schick, P Lewis, X Chen, G Izacard, S Riedel, H Hajishirzi, ... arXiv preprint arXiv:2211.09260, 2022 | 107 | 2022 |
| NeurIPS 2020 EfficientQA competition: Systems, analyses and lessons learned S Min, J Boyd-Graber, C Alberti, D Chen, E Choi, M Collins, K Guu, ... PMLR Volume 133, 2021 | 78 | 2021 |
| Salient phrase aware dense retrieval: Can a dense retriever imitate a sparse one? X Chen, K Lakhotia, B Oğuz, A Gupta, P Lewis, S Peshterliev, Y Mehdad, ... arXiv preprint arXiv:2110.06918, 2021 | 70 | 2021 |
Sebastian RiedelHonorary Professor @ University College London, Researcher @ DeepMindVerified email at cs.ucl.ac.uk
