  Zero-shot dialogue understanding aims to enable dialogue to track the user's
needs without any training data, which has gained increasing attention. In this
work, we investigate the understanding ability of ChatGPT for zero-shot
dialogue understanding tasks including spoken language understanding (SLU) and
dialogue state tracking (DST). Experimental results on four popular benchmarks
reveal the great potential of ChatGPT for zero-shot dialogue understanding. In
addition, extensive analysis shows that ChatGPT benefits from the multi-turn
interactive prompt in the DST task but struggles to perform slot filling for
SLU. Finally, we summarize several unexpected behaviors of ChatGPT in dialogue
understanding tasks, hoping to provide some insights for future research on
building zero-shot dialogue understanding systems with Large Language Models
(LLMs).
