  Advancements in large language models (LLMs) are revolutionizing interactive
game design, enabling dynamic plotlines and interactions between players and
non-player characters (NPCs). However, LLMs may exhibit flaws such as
hallucinations, forgetfulness, or misinterpretations of prompts, causing
logical inconsistencies and unexpected deviations from intended designs.
Automated techniques for detecting such game bugs are still lacking. To address
this, we propose a systematic LLM-based method for automatically identifying
such bugs from player game logs, eliminating the need for collecting additional
data such as post-play surveys. Applied to a text-based game DejaBoom!, our
approach effectively identifies bugs inherent in LLM-powered interactive games,
surpassing unstructured LLM-powered bug-catching methods and filling the gap in
automated detection of logical and design flaws.
