On LLM Wizards: Identifying Large Language Models' Behaviors for Wizard of Oz Experiments

The Wizard of Oz (WoZ) method is a widely adopted research approach where a human Wizard "role-plays" a not readily available technology and interacts with participants to elicit user behaviors and probe the design space. With the growing ability for modern large language models (LLMs) to role-play, one can apply LLMs as Wizards in WoZ experiments with better scalability and lower cost than the traditional approach. However, methodological guidance on responsibly applying LLMs in WoZ experiments and a systematic evaluation of LLMs' role-playing ability are lacking. Through two LLM-powered WoZ studies, we take the first step towards identifying an experiment lifecycle for researchers to safely integrate Figure 1: An overview of our proposed experiment lifecycle LLMs into WoZ experiments and interpret data generated compared to traditional Wizard of Oz experiments. We ask from settings that involve Wizards role-played by LLMs. We also GPT-4 empowered agents to play the role of "Wizards" in contribute a heuristic-based evaluation framework that allows the conversation-based Wizard of Oz experiments. The agents estimation of LLMs' role-playing ability in WoZ experiments and talk to either Simulacrums powered by GPT-4 (in Study 1) or reveals LLMs' behavior patterns at scale.