In light of recent advances in large language models (LLMs), the expectations for the next generation of virtual assistants include enhanced naturalness and adaptability across diverse usage scenarios. However, the creation of high-quality annotated data for Task-Oriented Dialog (TOD) is recognized to be slow and costly. To address these challenges, we introduce Task-Oriented Automatic Dialogs (TOAD), a novel and scalable TOD dataset along with its automatic generation pipeline. The TOAD dataset simulates realistic app context interaction and provide a variety of system response style options. Two aspects of system response styles are considered, verbosity level and users' expression mirroring. We benchmark TOAD on two response generation tasks, and the results show that modeling more verbose responses or responses without user expression mirroring is more challenging.
@article{arxiv.2402.10137,
title = {TOAD: Task-Oriented Automatic Dialogs with Diverse Response Styles},
author = {Yinhong Liu and Yimai Fang and David Vandyke and Nigel Collier},
journal= {arXiv preprint arXiv:2402.10137},
year = {2024}
}