We introduce a novel approach FnCTOD, to address zero-shot DST with LLMs. Our method seamlessly integrates DST as a part of the assistant's output dur

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2024-05-01 00:00:17

We introduce a novel approach FnCTOD, to address zero-shot DST with LLMs. Our method seamlessly integrates DST as a part of the assistant's output during chat completion. Specifically, we treat the schema of each task-oriented dialogue domain as a specific function, and DST for this domain as the process of ``calling'' the corresponding function. We thus instruct LLMs to generate function calls along with the response in the assistant's output. To achieve this, we convert the domain schema into function specifications, which include the function's description and required arguments, and incorporate them into the system prompt of the LLM. Additionally, we integrate these function calls into the assistant's output within the dialogue context.

Zero-shot DST performance comparison among (1) previous domain transfer approaches using small models; (2) previous prompting approaches exclusively relying on advanced proprietary LLMs; and (3) our approach, compatible with various LLMs, empowers various 7B and 13B models for superior performance and sets new state-of-the-art with GPT-4.

The detailed instruction for preparing the benchmark dataset MultiWOZ and pre-training corpora (optional) are provided in the ./data folder.