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).
翻译：零-shot对话理解旨在使对话能够无需任何训练数据地跟踪用户需求，这受到了越来越多的关注。在这项工作中，我们研究了ChatGPT在包括口语理解（SLU）和对话状态跟踪（DST）在内的零-shot对话理解任务中的理解能力。在四个流行的基准测试上的实验结果表明，ChatGPT具有在零-shot对话理解中的巨大潜力。此外，广泛的分析显示，ChatGPT受益于DST任务中的多轮互动提示，但很难执行SLU的孔填充。最后，我们总结了ChatGPT在对话理解任务中的几种意外行为，希望为未来构建基于大型语言模型（LLMs）的零-shot对话理解系统提供一些见解。
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