| 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 | 4702 | 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 | 3325 | 2020 |
| Language models as knowledge bases? F Petroni, T Rocktäschel, P Lewis, A Bakhtin, Y Wu, AH Miller, S Riedel EMNLP 2019, 2019 | 2823 | 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 | 620* | 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 | 499 | 2020 |
| Mlqa: Evaluating cross-lingual extractive question answering P Lewis, B Oğuz, R Rinott, S Riedel, H Schwenk ACL 2020, 2019 | 477 | 2019 |
| How Context Affects Language Models' Factual Predictions F Petroni, P Lewis, A Piktus, T Rocktäschel, Y Wu, AH Miller, S Riedel AKBC 2020, 2020 | 240 | 2020 |
| 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 | 239 | 2020 |
| Question and answer test-train overlap in open-domain question answering datasets P Lewis, P Stenetorp, S Riedel EACL 2021, 2020 | 202 | 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 | 201 | 2021 |
| Unsupervised question answering by cloze translation P Lewis, L Denoyer, S Riedel ACL 2019, 2019 | 183 | 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 | 179 | 2018 |
| Unsupervised question decomposition for question answering E Perez, P Lewis, W Yih, K Cho, D Kiela EMNLP 2020, 2020 | 170 | 2020 |
| Autoregressive search engines: Generating substrings as document identifiers M Bevilacqua, G Ottaviano, P Lewis, W Yih, S Riedel, F Petroni NeurIPS 2022, 2022 | 148 | 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 | 143* | 2020 |
| 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 | 109 | 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 | 90 | 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 | 74 | 2021 |
| Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. arXiv 2005 PSH Lewis, E Perez, A Piktus, F Petroni, V Karpukhin, N Goyal, H Küttler, ... arXiv preprint arXiv:2005.11401, 0 | 70 | |
| Domain-matched pre-training tasks for dense retrieval B Oğuz, K Lakhotia, A Gupta, P Lewis, V Karpukhin, A Piktus, X Chen, ... NAACL Findings 2022, 2021 | 60 | 2021 |
Sebastian RiedelHonorary Professor @ University College London, Researcher @ DeepMindEmail verificata su cs.ucl.ac.uk
