A Preliminary Evaluation of ChatGPT for Zero-shot Dialogue Understanding

Wenbo Pan, Qiguang Chen, Xiao Xu, Wanxiang Che, Libo Qin
arXiv preprint, 2023 ·

TL;DR: Zero-shot ChatGPT reaches 60.28 JGA on MultiWOZ 2.1 dialogue state tracking, nearly double GPT-3.5’s 32.25 and within one point of the fine-tuned state of the art (61.02).

One of the earliest systematic evaluations of ChatGPT on dialogue understanding (slot filling, intent detection, DST), documenting failure modes that shaped later instruction-tuning work. Cited 76 times.

Abstract

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).

BibTeX

@article{pan2023preliminary,
  title={A Preliminary Evaluation of ChatGPT for Zero-shot Dialogue Understanding},
  author={Pan, Wenbo and Chen, Qiguang and Xu, Xiao and Che, Wanxiang and Qin, Libo},
  journal={arXiv preprint arXiv:2304.04256},
  year={2023}
}

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