Patrick Lewis
Patrick Lewis
UCL, Research Scientist Cohere
Verified email at - Homepage
Cited by
Cited by
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
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
Language models as knowledge bases?
F Petroni, T Rocktäschel, P Lewis, A Bakhtin, Y Wu, AH Miller, S Riedel
EMNLP 2019, 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
Mlqa: Evaluating cross-lingual extractive question answering
P Lewis, B Oğuz, R Rinott, S Riedel, H Schwenk
ACL 2020, 2019
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
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
How Context Affects Language Models' Factual Predictions
F Petroni, P Lewis, A Piktus, T Rocktäschel, Y Wu, AH Miller, S Riedel
AKBC 2020, 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
Question and answer test-train overlap in open-domain question answering datasets
P Lewis, P Stenetorp, S Riedel
EACL 2021, 2020
Unsupervised question answering by cloze translation
P Lewis, L Denoyer, S Riedel
ACL 2019, 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
Unsupervised question decomposition for question answering
E Perez, P Lewis, W Yih, K Cho, D Kiela
EMNLP 2020, 2020
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
Autoregressive search engines: Generating substrings as document identifiers
M Bevilacqua, G Ottaviano, P Lewis, W Yih, S Riedel, F Petroni
NeurIPS 2022, 2022
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
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
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
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
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
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