LAD: Language Models as Data for Zero-Shot Dialog

TL;DR

This paper introduces LAD, a method using GPT-3 to generate diverse synthetic data for zero-shot task-oriented dialog, significantly improving model performance without relying on human-labeled data.

Contribution

LAD presents a novel approach to generate synthetic dialog data with structural constraints using GPT-3, enhancing zero-shot dialog model training.

Findings

+15% intent prediction accuracy

+31.4 F-1 slot filling improvement

Training with LAD is competitive with human dialogs

Abstract

To facilitate zero-shot generalization in taskoriented dialog, this paper proposes Language Models as Data (LAD). LAD is a paradigm for creating diverse and accurate synthetic data which conveys the necessary structural constraints and can be used to train a downstream neural dialog model. LAD leverages GPT-3 to induce linguistic diversity. LAD achieves significant performance gains in zero-shot settings on intent prediction (+15%), slot filling (+31.4 F-1) and next action prediction (+11 F1). Furthermore, an interactive human evaluation shows that training with LAD is competitive with training on human dialogs. LAD is open-sourced, with the code and data available at https://github.com/Shikib/lad.