Germán Kruszewski
Germán Kruszewski
Senior Scientist @ Naver Labs Europe
Verified email at - Homepage
Cited by
Cited by
Don’t count, predict! a systematic comparison of context-counting vs. context-predicting semantic vectors
M Baroni, G Dinu, G Kruszewski
Proceedings of the 52nd Annual Meeting of the Association for Computational …, 2014
Bloom: A 176b-parameter open-access multilingual language model
BS Workshop, TL Scao, A Fan, C Akiki, E Pavlick, S Ilić, D Hesslow, ...
arXiv preprint arXiv:2211.05100, 2022
What you can cram into a single vector: Probing sentence embeddings for linguistic properties
A Conneau, G Kruszewski, G Lample, L Barrault, M Baroni
Proceedings of the 56th Annual Meeting of the Association for Computational …, 2018
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ...
arXiv preprint arXiv:2206.04615, 2022
The LAMBADA dataset: Word prediction requiring a broad discourse context
D Paperno, G Kruszewski, A Lazaridou, QN Pham, R Bernardi, S Pezzelle, ...
Proceedings of the 54th Annual Meeting of the Association for Computational …, 2016
How cosmopolitan are emojis? Exploring emojis usage and meaning over different languages with distributional semantics
F Barbieri, G Kruszewski, F Ronzano, H Saggion
Proceedings of the 24th ACM international conference on Multimedia, 531-535, 2016
The emergence of number and syntax units in LSTM language models
Y Lakretz, G Kruszewski, T Desbordes, D Hupkes, S Dehaene, M Baroni
Proceedings of the 2019 Conference of the North American Chapter of the …, 2019
Memorize or generalize? searching for a compositional rnn in a haystack
A Liška, G Kruszewski, M Baroni
arXiv preprint arXiv:1802.06467, 2018
Convolutional neural network language models
NQ Pham, G Kruszewski, G Boleda
Proceedings of the 2016 conference on empirical methods in natural language …, 2016
Cooperative learning of disjoint syntax and semantics
S Havrylov, G Kruszewski, A Joulin
Proceedings of the 2019 Conference of the North American Chapter of the …, 2019
Deriving boolean structures from distributional vectors
G Kruszewski, D Paperno, M Baroni
Transactions of the Association for Computational Linguistics 3, 375-388, 2015
Generating grammar exercises
L Perez-Beltrachini, C Gardent, G Kruszewski
Proceedings of the Seventh Workshop on Building Educational Applications …, 2012
Learning compositionally through attentive guidance
D Hupkes, A Singh, K Korrel, G Kruszewski, E Bruni
arXiv preprint arXiv:1805.09657, 2018
There is no logical negation here, but there are alternatives: modeling conversational negation with distributional semantics
G Kruszewski, D Paperno, R Bernardi, M Baroni
Computational Linguistics special issue: Formal Distributional Semantics, 2016
Jointly optimizing word representations for lexical and sentential tasks with the C-PHRASE model
N Pham, G Kruszewski, A Lazaridou, M Baroni
Proceedings of the 53rd Annual Meeting of the Association for Computational …, 2015
Aligning language models with preferences through f-divergence minimization
D Go, T Korbak, G Kruszewski, J Rozen, N Ryu, M Dymetman
arXiv preprint arXiv:2302.08215, 2023
So similar and yet incompatible: Toward the automated identification of semantically compatible words
G Kruszewski, M Baroni
Proceedings of the 2015 Conference of the North American Chapter of the …, 2015
Unsupervised and distributional detection of machine-generated text
M Gallé, J Rozen, G Kruszewski, H Elsahar
arXiv preprint arXiv:2111.02878, 2021
On reinforcement learning and distribution matching for fine-tuning language models with no catastrophic forgetting
T Korbak, H Elsahar, G Kruszewski, M Dymetman
Advances in Neural Information Processing Systems 35, 16203-16220, 2022
CommAI: Evaluating the first steps towards a useful general AI
M Baroni, A Joulin, A Jabri, G Kruszewski, A Lazaridou, K Simonic, ...
arXiv preprint arXiv:1701.08954, 2017
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