Few-shot Learning with Multilingual Language Models

TL;DR

This paper trains large multilingual language models to improve few- and zero-shot learning across diverse languages, achieving state-of-the-art results in multilingual reasoning, inference, and translation tasks, while analyzing cross-lingual transfer methods.

Contribution

It introduces a large-scale multilingual generative language model that outperforms GPT-3 in multiple multilingual tasks and provides insights into effective cross-lingual prompting strategies.

Findings

State-of-the-art few-shot performance in over 20 languages.

Outperforms GPT-3 in multilingual reasoning and inference.

Excels in machine translation with limited training examples.

Abstract

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.