Few-shot Learning with Multilingual Language Models
This work addresses the limitation of English-dominated training data for multilingual AI models, offering improved few-shot learning capabilities across languages, though it is incremental as it builds on existing large-scale language model paradigms.
The paper tackles the problem of cross-lingual generalization in few-shot learning by training multilingual generative language models on diverse languages, resulting in state-of-the-art performance in over 20 languages with improvements such as +7.4% accuracy in 0-shot commonsense reasoning and surpassing GPT-3 in 171 out of 182 machine translation directions.
Large-scale generative language models such as GPT-3 are competitive few-shot learners. While these models are known to be able to jointly represent many different languages, their training data is dominated by English, potentially limiting their cross-lingual generalization. In this work, we train multilingual generative language models on a corpus covering a diverse set of languages, and study their few- and zero-shot learning capabilities in a wide range of tasks. Our largest model with 7.5 billion parameters sets new state of the art in few-shot learning in more than 20 representative languages, outperforming GPT-3 of comparable size in multilingual commonsense reasoning (with +7.4% absolute accuracy improvement in 0-shot settings and +9.4% in 4-shot settings) and natural language inference (+5.4% in each of 0-shot and 4-shot settings). On the FLORES-101 machine translation benchmark, our model outperforms GPT-3 on 171 out of 182 directions with 32 training examples, while surpassing the official supervised baseline in 45 directions. We conduct an in-depth analysis of different multilingual prompting approaches, showing in particular that strong few-shot learning performance across languages can be achieved via cross-lingual transfer through both templates and demonstration examples. Finally, we evaluate our models in social value tasks such as hate speech detection in five languages and find it has limitations similar to comparable sized GPT-3 models.