In-Context Learning User Simulators for Task-Oriented Dialog Systems
Silvia Terragni Modestas Filipavicius Nghia Khau Bruna Guedes André Manso Roland Mathis
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.