This paper presents a novel application of large language models in user simulation for task-oriented dialog systems, specifically focusing on an in-context learning approach. By harnessing the power of these models, the proposed approach generates diverse utterances based on user goals and limited dialog examples. Unlike traditional simulators, this method eliminates the need for labor-intensive rule definition or extensive annotated data, making it more efficient and accessible. Additionally, an error analysis of the interaction between the user simulator and dialog system uncovers common mistakes, providing valuable insights into areas that require improvement. Our implementation is available at https://github.com/telepathylabsai/prompt-based-user-simulator.

翻译：本文提出了一种大型语言模型在任务型对话系统用户模拟中的新颖应用，重点聚焦于上下文学习方法。通过利用这些模型的能力，所提出的方法能够基于用户目标及有限的对话样例生成多样化的语句。与传统模拟器相比，该方法无需耗费大量人力定义规则或依赖大规模标注数据，从而提升了效率与可及性。此外，对用户模拟器与对话系统交互过程中的错误分析揭示了常见失误，为需改进的领域提供了宝贵见解。我们的实现代码位于 https://github.com/telepathylabsai/prompt-based-user-simulator。