Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective
This provides a more efficient and generalizable approach to zero-shot learning for NLP practitioners, though it is incremental in improving existing methods.
The paper tackles zero-shot learning for natural language understanding by converting tasks into multiple-choice formats, achieving state-of-the-art performance on benchmarks like natural language inference and text classification with only 235M parameters, significantly smaller than billion-parameter models.
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 .