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- sl:arxiv_author :
- sl:arxiv_firstAuthor : Victor Sanh
- sl:arxiv_num : 2110.08207
- sl:arxiv_published : 2021-10-15T17:08:57Z
- sl:arxiv_summary : Large language models have recently been shown to attain reasonable zero-shot
generalization on a diverse set of tasks. It has been hypothesized that this is
a consequence of implicit multitask learning in language model training. Can
zero-shot generalization instead be directly induced by explicit multitask
learning? To test this question at scale, we develop a system for easily
mapping general natural language tasks into a human-readable prompted form. We
convert a large set of supervised datasets, each with multiple prompts using
varying natural language. These prompted datasets allow for benchmarking the
ability of a model to perform completely unseen tasks specified in natural
language. We fine-tune a pretrained encoder-decoder model on this multitask
mixture covering a wide variety of tasks. The model attains strong zero-shot
performance on several standard datasets, often outperforming models 16x its
size. Further, our approach attains strong performance on a subset of tasks
from the BIG-Bench benchmark, outperforming models 6x its size. All prompts and
trained models are available at github.com/bigscience-workshop/promptsource/.@en
- sl:arxiv_title : Multitask Prompted Training Enables Zero-Shot Task Generalization@en
- sl:arxiv_updated : 2021-10-15T17:08:57Z
- sl:bookmarkOf : https://arxiv.org/abs/2110.08207
- sl:creationDate : 2021-10-18
- sl:creationTime : 2021-10-18T23:12:20Z
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