On LLM Wizards: Identifying Large Language Models' Behaviors for Wizard of Oz Experiments
This work addresses the problem of scalable and cost-effective WoZ experiments for researchers, but it is incremental as it builds on existing WoZ methods with LLMs.
The paper tackles the lack of methodological guidance for using large language models (LLMs) as Wizards in Wizard of Oz experiments by proposing an experiment lifecycle for safe integration and a heuristic-based evaluation framework to assess LLMs' role-playing abilities and behavior patterns.
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 LLMs into WoZ experiments and interpret data generated from settings that involve Wizards role-played by LLMs. We also contribute a heuristic-based evaluation framework that allows the estimation of LLMs' role-playing ability in WoZ experiments and reveals LLMs' behavior patterns at scale.