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 or responses without user expression mirroring
is more challenging.

鉴于大型语言模型的最新进展，下一代虚拟助手的期望包括在各种使用场景中增强自然性和适应性。为了解决为面向任务的对话（TOD）创建高质量的注释数据的缓慢和昂贵问题，我们介绍了面向任务的自动对话（TOAD），以及其自动生成流程。TOAD 数据集模拟了真实的应用程序上下文交互，并提供了多种系统响应样式选项。我们在两个响应生成任务上对 TOAD 进行了基准测试，结果表明，对建模更冗长或没有用户表情的响应更具挑战性。