Cross-Lingual Supervision improves Large Language Models Pre-training

• URL: http://arxiv.org/abs/2305.11778v1
• Date: Fri, 19 May 2023 16:14:07 GMT
• Title: Cross-Lingual Supervision improves Large Language Models Pre-training
• Authors: Andrea Schioppa and Xavier Garcia and Orhan Firat
• Abstract summary: We demonstrate that pre-training Large Language Models on a mixture of a self-supervised Language Modeling objective and the supervised Machine Translation objective, yields models with better in-context learning abilities. As pre-training is a very resource-intensive process and a grid search on the best mixing ratio between the two objectives is prohibitively expensive, we propose a simple yet effective strategy to learn it during pre-training.
• Score: 36.932380291416365
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The recent rapid progress in pre-training Large Language Models has relied on using self-supervised language modeling objectives like next token prediction or span corruption. On the other hand, Machine Translation Systems are mostly trained using cross-lingual supervision that requires aligned data between source and target languages. We demonstrate that pre-training Large Language Models on a mixture of a self-supervised Language Modeling objective and the supervised Machine Translation objective, therefore including cross-lingual parallel data during pre-training, yields models with better in-context learning abilities. As pre-training is a very resource-intensive process and a grid search on the best mixing ratio between the two objectives is prohibitively expensive, we propose a simple yet effective strategy to learn it during pre-training.

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