Inference is All You Need: Self Example Retriever for Cross-domain Dialogue State Tracking with ChatGPT
This provides a scalable and adaptable solution for dialogue state tracking across domains, though it is incremental as it builds on existing in-context learning methods.
The paper tackles the problem of domain transfer in dialogue state tracking by using ChatGPT for inference and in-context learning without training, achieving competitive performance on the MultiWOZ dataset.
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.