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.

翻译：构建端到端任务机器人并以最少的人力投入维护其与新功能的集成，是对话研究领域长期存在的挑战。近年来，大型语言模型（LLMs）在跨多种下游任务中展现了卓越的对话参与能力和指令遵循能力。在本工作中，我们提出SGP-TOD，一种基于LLMs的模式引导提示方法，用于轻松构建任务导向型对话系统。利用符号化知识——任务模式，我们指导固定的LLMs在新任务上生成合适的回复，从而避免对训练数据的依赖。具体而言，SGP-TOD包含三个组件：一个与用户交互的LLM、一个辅助LLM进行对话状态追踪的DST提示器（其输出随后用于检索数据库条目），以及一个根据给定对话策略生成恰当回复的策略提示器。在Multiwoz、RADDLE和STAR数据集上的实验结果表明，我们无需训练的策略SGP-TOD，在没有任何任务特定数据的情况下，取得了最先进的（SOTA）零样本性能，并大幅超越了少样本方法。在领域扩展设置下，SGP-TOD仅需添加额外的模式规则即可灵活适应新功能。我们已将代码和数据集公开。