Generating Training Data with Language Models: Towards Zero-Shot Language Understanding
This enables zero-shot learning for NLU tasks without task-specific data, addressing a bottleneck in language model applications, though it is incremental as it builds on existing PLM capabilities.
The paper tackles zero-shot natural language understanding by generating class-conditioned training data with a unidirectional language model and fine-tuning a bidirectional model, achieving strong performance on GLUE tasks, such as 72.3/73.8 on MNLI-m/mm and 92.8 on SST-2, comparable to few-shot methods with 32 samples per class.
Pretrained language models (PLMs) have demonstrated remarkable performance in various natural language processing tasks: Unidirectional PLMs (e.g., GPT) are well known for their superior text generation capabilities; bidirectional PLMs (e.g., BERT) have been the prominent choice for natural language understanding (NLU) tasks. While both types of models have achieved promising few-shot learning performance, their potential for zero-shot learning has been underexplored. In this paper, we present a simple approach that uses both types of PLMs for fully zero-shot learning of NLU tasks without requiring any task-specific data: A unidirectional PLM generates class-conditioned texts guided by prompts, which are used as the training data for fine-tuning a bidirectional PLM. With quality training data selected based on the generation probability and regularization techniques (label smoothing and temporal ensembling) applied to the fine-tuning stage for better generalization and stability, our approach demonstrates strong performance across seven classification tasks of the GLUE benchmark (e.g., 72.3/73.8 on MNLI-m/mm and 92.8 on SST-2), significantly outperforming zero-shot prompting methods and achieving even comparable results to strong few-shot approaches using 32 training samples per class.