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Status: Unchecked ×Keyword: mT5 ×Clear all

5 claims from 2 papers

1. Computer Science › Topic Modeling

   mT5: A massively multilingual pre-trained text-to-text transformer

   Xue, Constant, Adam et al. · arXiv (Cornell University) · 2020

   Unchecked2 claims
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   1. Unchecked“We detail the design and modified training of mT5 and demonstrate its state-of-the-art performance on many multilingual benchmarks.”
   2. Unchecked“We also describe a simple technique to prevent "accidental translation" in the zero-shot setting, where a generative model chooses to (partially) translate its prediction into the wrong language.”

2. Computer Science › Topic Modeling

   Crosslingual Generalization through Multitask Finetuning

   Muennighoff, Thomas, Sutawika et al. · arXiv (Cornell University) · 2022

   The authors finetune multilingual BLOOM and mT5 models on many prompted tasks, producing BLOOMZ and mT0, and study how well they generalise zero-shot across languages.

   Unchecked3 claims
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   1. UncheckedFinetuning large multilingual models on English tasks with English prompts can transfer to non-English languages seen only in pretraining.“We find finetuning large multilingual language models on English tasks with English prompts allows for task generalization to non-English languages that appear only in the pretraining corpus.”
   2. UncheckedFinetuning multilingual models on multilingual tasks with English prompts further improves English and non-English performance, giving state-of-the-art zero-shot results.“Finetuning on multilingual tasks with English prompts further improves performance on English and non-English tasks leading to various state-of-the-art zero-shot results.”
   3. UncheckedFinetuned multilingual models can perform new tasks zero-shot in languages they were never intentionally exposed to, according to the paper.“Surprisingly, we find models are capable of zero-shot generalization to tasks in languages they have never intentionally seen.”