SELF-GUIDED NOISE-FREE DATA GENERATION FOR EFFICIENT ZERO-SHOT LEARNING

Abstract

There is a rising interest in further exploring the zero-shot learning potential of large pre-trained language models (PLMs). A new paradigm called data-generationbased zero-shot learning has achieved impressive success. In this paradigm, the synthesized data from the PLM acts as the carrier of knowledge, which is used to train a task-specific model with orders of magnitude fewer parameters than the PLM, achieving both higher performance and efficiency than prompt-based zero-shot learning methods on PLMs. The main hurdle of this approach is that the synthesized data from PLM usually contains a significant portion of low-quality samples. Fitting on such data will greatly hamper the performance of the taskspecific model, making it unreliable for deployment. Previous methods remedy this issue mainly by filtering synthetic data using heuristic metrics(e.g., output confidence), or refining the data with the help of a human expert, which comes with excessive manual tuning or expensive costs. In this paper, we propose a novel noise-robust re-weighting framework SUNGEN to automatically construct high-quality data for zero-shot classification problems. Our framework features the ability to learn the sample weights indicating data quality without requiring any human annotation. We theoretically and empirically verify the ability of our method to help construct good-quality synthetic datasets. Notably, SUNGEN-LSTM yields a 9.8% relative improvement than the baseline on average accuracy across eight different established text classification tasks.

1. INTRODUCTION

Owing to the superior generative capacity of large-scale pre-trained language models (PLMs), there has been an emerging trend of using these powerful models (e.g., GPT) to generate training data for downstream tasks (Anaby-Tavor et al., 2020; Puri et al., 2020; Kumar et al., 2020; Lee et al., 2021, inter alia) . Among them, a new line of generation-based zero-shot learning using the unfinetuned PLM pushes the envelope further (Schick & Schütze, 2021; Ye et al., 2022a; Meng et al., 2022) , featuring total annotation-free training for downstream tasks. Ye et al. (2022a) (ZEROGEN) further boosts the efficiency by using the generated data to train tiny task models (TAM), which have ordersof-magnitude fewer parameters than the PLM. Specifically, they first design prompts incorporating the task description and label information, then use them to guide the data generation from the PLM. Subsequently, the synthesized dataset is used to train the tiny task-specific models. Compared with the classic prompt-based zero-shot learning on PLM, this new paradigm enjoys two favorable properties: (1) since the task model has orders-of-magnitude fewer parameters than the PLM, it demonstrates much lower inference latency; (2) with the large amount of PLM-generated training data, the task model often shows better performance than prompt-based zero-shot PLM counterparts. In the above paradigm, the amount and the quality of the generated data are crucial factors for the task model's performance. Unfortunately, despite the unlimited training data that one can generate * Equal Contribution. Code is available at this link. 1

