Cross-Lingual Supervision improves Large Language Models Pre-training
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.
646ae99d31fbfc5df8202a47
Andrea Schioppa
extracted_pending
2024-04-23T14:01:37.285Z
6627bf2b8c9f64932fbb13dc
/avatars/626fa8a5d8e6a9b59d51e580d26f4c3c.svg
Andrea Schioppa
user
arischioppa
646ae99d31fbfc5df8202a48
Xavier Garcia
646ae99d31fbfc5df8202a49
Orhan Firat
Cross-Lingual Supervision improves Large Language Models Pre-training
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.
Combining self-supervised and supervised objectives during pre-training of Large Language Models improves their in-context learning by incorporating cross-lingual parallel data.
Large Language Models self-supervised language modeling next token prediction span corruption Machine Translation Systems cross-lingual supervision in-context learning pre-training supervised Machine Translation objective