Scaling up the size and training of autoregressive language models has enabled novel ways of solving Natural Language Processing tasks using zero-shot and few-shot learning. While extreme-scale language models such as GPT-3 offer multilingual capabilities, zero-shot learning for languages other than English remain largely unexplored. Here, we introduce Cedille, a large open source auto-regressive language model, specifically trained for the French language. Our results show that Cedille outperforms existing French language models and is competitive with GPT-3 on a range of French zero-shot benchmarks. Furthermore, we provide an in-depth comparison of the toxicity exhibited by these models, showing that Cedille marks an improvement in language model safety thanks to dataset filtering.
Cedille: A large autoregressive French language model
Cedille, an auto-regressive language model for French, outperforms existing models and is competitive with GPT-3 on zero-shot benchmarks while improving language model safety through dataset filtering.
- Year
- 2022
- Venue
- arXiv 2022
- Authors
- 2
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- Abstract onlyARXIV-DEFAULT
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- arxiv.org/abs/2202.03371ARXIV-DEFAULT
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