  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.
