A Preliminary Evaluation of ChatGPT for Zero-shot Dialogue Understanding
This work provides insights for building zero-shot dialogue systems with LLMs, but it is incremental as it evaluates an existing model on standard tasks.
The paper evaluated ChatGPT's zero-shot performance on dialogue understanding tasks, finding it shows great potential on benchmarks but struggles with slot filling in spoken language understanding.
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).