Summary of Croissantllm: a Truly Bilingual French-english Language Model, by Manuel Faysse et al.
CroissantLLM: A Truly Bilingual French-English Language Model
by Manuel Faysse, Patrick Fernandes, Nuno M. Guerreiro, António Loison, Duarte M. Alves, Caio Corro, Nicolas Boizard, João Alves, Ricardo Rei, Pedro H. Martins, Antoni Bigata Casademunt, François Yvon, André F.T. Martins, Gautier Viaud, Céline Hudelot, Pierre Colombo
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces CroissantLLM, a 1.3B language model that can process both English and French text. The model is trained on a dataset containing 3T tokens and is designed to run efficiently on consumer-grade hardware. To train the model, the authors use a custom tokenizer and bilingual finetuning datasets, with a ratio of 1:1 between English and French data. The authors also release the training dataset, which includes a manually curated French split with varied sources of high-quality data. To evaluate the model’s performance outside of English, the authors create a novel benchmark called FrenchBench, which consists of classification and generation tasks that cover various aspects of model performance in French. Additionally, the authors release codebases, checkpoints, and fine-tuned models to foster further research into large language models. The evaluation framework used is FMTI, which measures transparency and validation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new language model called CroissantLLM that can understand both English and French. It’s like having two languages in one! To make it work, the researchers trained the model on lots of text data from both languages. They also made sure to include some high-quality French texts to help the model learn nuances of the French language. The model is fast and works well on regular computers, not just super-powerful ones. The authors are being open and sharing their code and models with others so that more research can be done. |
Keywords
* Artificial intelligence * Classification * Language model * Tokenizer
