用ChatGPT推理实现零参数跨域对话状态跟踪
Inference is All You Need: Self Example Retriever for Cross-domain Dialogue State Tracking with ChatGPT
- 通过提示工程引导ChatGPT思维链，动态检索相关示例
- 在MultiWOZ上达到与有监督方法相当的跨域泛化性能
- 无需训练参数，适合快速部署到新领域
传统对话状态跟踪方法严重依赖大量标注数据和手工特征，限制了其可扩展性和新领域的适应能力。本文提出一种新方法，利用ChatGPT的推理与上下文学习能力，在不更新任何参数的前提下实现对话状态跟踪的领域迁移。通过引导ChatGPT的思维链，使其能够检索相关示例并泛化知识，仅通过推理即可准确推断对话状态。在MultiWOZ数据集上的实验结果表明，该方法具有竞争力的性能和良好的跨域泛化能力。无参数的方法提供了可扩展且适应性强的解决方案，为领域迁移学习开辟了新方向。
原文摘要 · Abstract (English)
Traditional dialogue state tracking approaches heavily rely on extensive training data and handcrafted features, limiting their scalability and adaptability to new domains. In this paper, we propose a novel method that leverages inference and in-context learning with ChatGPT for domain transfer in dialogue state tracking, without any parameter updates. By guiding ChatGPT's chain of thought, we enable it to retrieve relevant examples and generalize knowledge to accurately infer dialogue states, solely through inference. Experimental results on the MultiWOZ dataset demonstrate competitive performance and promising generalization across domains. Our parameter-free approach offers a scalable and adaptable solution, opening new research directions in domain transfer learning.
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