This paper explores a simple method for improving the zero-shot learning abilities of
language models. We show that instruction tuning--finetuning language models on a�…

Large language models have recently been shown to attain reasonable zero-shot
generalization on a diverse set of tasks (Brown et al., 2020). It has been hypothesized that�…

Large language models have recently been shown to attain reasonable zero-shot
generalization on a diverse set of tasks (Brown et al., 2020). It has been hypothesized that�…

Large pre-trained language models (LMs) such as GPT-3 have acquired a surprising ability
to perform zero-shot learning. For example, to classify sentiment without any training�…

How can we extend a pre-trained model to many language understanding tasks, without
labeled or additional unlabeled data? Pre-trained language models (PLMs) have been�…

One of the most impressive results of recent NLP history is the ability of pre-trained language
models to solve new tasks in a zero-shot setting. To achieve this, NLP tasks are framed as�…

Instruction tuning, a new learning paradigm that fine-tunes pre-trained language models on
tasks specified through instructions, has shown promising zero-shot performance on various�…

Large pretrained Transformer language models have been shown to exhibit zero-shot
generalization, ie they can perform a wide variety of tasks that they were not explicitly trained�…

Recently, a boom of papers has shown extraordinary progress in zero-shot and few-shot
learning with various prompt-based models. It is commonly argued that prompts help models�…

We introduce a method to improve the zero-shot reasoning abilities of large language
models on general language understanding tasks. Specifically, we build an autonomous�…