  User Simulators play a pivotal role in training and evaluating task-oriented
dialogue systems. Traditional user simulators typically rely on
human-engineered agendas, resulting in generated responses that often lack
diversity and spontaneity. Although large language models (LLMs) exhibit a
remarkable capacity for generating coherent and contextually appropriate
utterances, they may fall short when tasked with generating responses that
effectively guide users towards their goals, particularly in dialogues with
intricate constraints and requirements. This paper introduces DuetSim, a novel
framework designed to address the intricate demands of task-oriented dialogues
by leveraging LLMs. DuetSim stands apart from conventional approaches by
employing two LLMs in tandem: one dedicated to response generation and the
other focused on verification. This dual LLM approach empowers DuetSim to
produce responses that not only exhibit diversity but also demonstrate accuracy
and are preferred by human users. We validate the efficacy of our method
through extensive experiments conducted on the MultiWOZ dataset, highlighting
improvements in response quality and correctness, largely attributed to the
incorporation of the second LLM. Our code is accessible at:
https://github.com/suntea233/DuetSim.
