  We propose a new paradigm for zero-shot learners that is format agnostic,
i.e., it is compatible with any format and applicable to a list of language
tasks, such as text classification, commonsense reasoning, coreference
resolution, and sentiment analysis. Zero-shot learning aims to train a model on
a given task such that it can address new learning tasks without any additional
training. Our approach converts zero-shot learning into multiple-choice tasks,
avoiding problems in commonly used large-scale generative models such as FLAN.
It not only adds generalization ability to models but also significantly
reduces the number of parameters. Our method shares the merits of efficient
training and deployment. Our approach shows state-of-the-art performance on
several benchmarks and produces satisfactory results on tasks such as natural
language inference and text classification. Our model achieves this success
with only 235M parameters, which is substantially smaller than state-of-the-art
models with billions of parameters. The code and pre-trained models are
available at https://github.com/IDEA-CCNL/Fengshenbang-LM .
