Shows multitask multilingual generalization in language model.
The crystallization of modeling methods around the Transformer architecture has been a boon for practitioners. Simple, well-motivated architectural variations that transfer across tasks and scale, increasing the impact of modeling research. However, …
Over 2,000 prompts for roughly 170 datasets are available through PromptSource framework.
T0 shows zero-shot task generalization on English natural language prompts, outperforming GPT-3 on many tasks, while being 16x smaller!
We propose a trasductive approach for few shot cross-lingual classification.
This talk summarizes the paper [`Finetuned Language Models Are Zero-Shot Learners`](https://arxiv.org/abs/2109.01652).
This talk summarizes the paper [`Language Models are Few-Shot Learners`](https://arxiv.org/abs/2005.14165).
We propose AugVic, a data augmentation framework for sequence to sequence model (i.e. NMT) using Language Model.
We propose UXLA, a novel data augmentation framework for self-supervised learning in zero-resource transfer learning scenarios.