A massively multilingual generative language model that follows instructions in 101 languages,trained by finetuning mT5.

Applies Multitask prompted fine tuning to the pretrained multilingual models on English tasks with English prompts to attain task generalization to non-English languages that appear only in the pretraining corpus.

Explores instruction fine tuning with a particular focus on scaling the number of tasks, scaling the model size, and fine tuning on chain-of-thought data.

A fine tuned encoder-decoder model on a multitask mixture covering a wide variety of tasks, attaining strong zero-shot performance on several standard datasets.

An instruction-tuned language model developed through finetuning on various NLP datasets described by natural language instructions.

A multilingual variant of T5 based on T5 v1.1, pre-trained on a new Common Crawl-based dataset covering 101 languages (mC4).

A unified encoder-decoder framework that converts all text-based language problems into a text-to-text format.