ZEROGEN: Efficient Zero-shot Learning via Dataset Generation

Jiacheng Ye, Jiahui Gao, Qintong Li, Jiangtao Feng, Zhiyong Wu, Tao Yu, Lingpeng Kong

Abstract:

There is a growing interest in dataset generation recently due to the superior generative
capacity of large pre-trained language models
(PLMs). In this paper, we study a flexible
and efficient zero-short learning method, ZEROGEN. Given a zero-shot task, we first
generate a dataset from scratch using PLMs
in an unsupervised manner. Then, we train
a tiny task model (e.g., LSTM) under the
supervision of the synthesized dataset. This
approach allows highly efficient inference as
the final task model only has orders of magnitude fewer parameters comparing to PLMs
(e.g., GPT2-XL). Apart from being annotationfree and efficient, we argue that ZEROGEN can
also provide useful insights from the perspective of data-free model-agnostic knowledge
distillation, and unreferenced text generation
evaluation. Experiments and analysis on different NLP tasks, namely, text classification,
question answering, and natural language inference), show the effectiveness of ZEROGEN.