Un modèle Transformer Génératif Pré-entrainé pour le _ _ _ _ _ _ français (Generative Pre-trained Transformer in _ _ _ _ _ _ (French) We introduce a French adaptation from the well-known GPT model)

By Antoine Simoulin, Benoit Crabb'e. 2021

Subfields: Large Language Models, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Large Language Models

1. Timo Schick
2. Colin Raffel
3. Pascale Fung
4. Luke S. Zettlemoyer
5. Douwe Kiela
6. Alexander M. Rush
7. Niklas Muennighoff
8. Yonatan Belinkov

In Natural Language Processing

1. Mohit Bansal
2. Yonatan Belinkov
3. Yejin Choi
4. Noah A. Smith
5. Sebastian Gehrmann
6. Graham Neubig
7. Samuel R. Bowman
8. Percy Liang

Citations in the corpus

• BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (2022, methodology): “We use the following baseline models where appropriate (e.g. in settings where they support the language of the evaluation dataset): • mGPT (Shliazhko et al., 2022), GPT-style models trained on 60 languages from Wikipedia and Common Crawl • GPT-Neo (Black et al., 2021), GPT-J-6B (Wang and Komatsuzak”