We introduce MADLAD-400, a manually audited, general domain 3T token
mon...
The recent rapid progress in pre-training Large Language Models has reli...
Pretrained multilingual large language models have typically used heuris...
In this work, we provide a large-scale empirical study of the scaling
pr...
Current benchmarks for evaluating neural code models focus on only a sma...
We demonstrate the potential of few-shot translation systems, trained wi...
Crosslingual conditional generation (e.g., machine translation) has long...
Scaling language models improves performance but comes with significant
...
We present FRMT, a new dataset and evaluation benchmark for Few-shot
Reg...
In this paper we share findings from our effort to build practical machi...
Large language models have been shown to achieve remarkable performance
...
Recent neural network-based language models have benefited greatly from
...
We explore the use of natural language prompts for controlling various
a...
Natural language understanding and generation models follow one of the t...
Achieving universal translation between all human language pairs is the
...
Style transfer is the task of rewriting an input sentence into a target ...
We present an empirical study of scaling properties of encoder-decoder
T...
In this work, we take the first steps towards building a universal rewri...
We propose a straightforward vocabulary adaptation scheme to extend the
...
Unsupervised translation has reached impressive performance on resource-...
This paper proposes a methodology to estimate stress in the subsurface b...
We present a probabilistic framework for multilingual neural machine
tra...