SGP-TOD: Building Task Bots Effortlessly via Schema-Guided LLM Prompting
Xiaoying Zhang Baolin Peng Kun Li Jingyan Zhou Helen Meng
Building end-to-end task bots and maintaining their integration with new
functionalities using minimal human efforts is a long-standing challenge in
dialog research. Recently large language models (LLMs) have demonstrated
exceptional proficiency in conversational engagement and adherence to
instructions across various downstream tasks. In this work, we introduce
SGP-TOD, Schema-Guided Prompting for building Task-Oriented Dialog systems
effortlessly based on LLMs. Utilizing the symbolic knowledge -- task schema, we
instruct fixed LLMs to generate appropriate responses on novel tasks,
circumventing the need for training data. Specifically, SGP-TOD comprises three
components: a LLM for engaging with users, a DST Prompter to aid the LLM with
dialog state tracking, which is then used to retrieve database items, and a
Policy Prompter to elicit proper responses adhering to the provided dialog
policy. Experimental results on Multiwoz, RADDLE and STAR datasets show that
our training-free strategy SGP-TOD, without any task-specific data, yields
state-of-the-art (SOTA) zero-shot performance, greatly surpasses the few-shot
approaches. In a domain-extension setting, SGP-TOD aptly adapts to new
functionalities by merely adding supplementary schema rules. We make our code
and data publicly available.