End-to-end Task-Oriented Dialog (TOD) systems typically require extensive training datasets to perform well. In contrast, large language model (LLM) based TOD systems can excel even with limited data due to their ability to learn tasks through in-context exemplars. However, these models lack alignment with the style of responses in training data and often generate comprehensive responses, making it difficult for users to grasp the information quickly. In response, we propose SyncTOD that synergizes LLMs with task-specific hints to improve alignment in low-data settings. SyncTOD employs small auxiliary models to provide hints and select exemplars for in-context prompts. With ChatGPT, SyncTOD achieves superior performance compared to LLM-based baselines and SoTA models in low-data settings, while retaining competitive performance in full-data settings.

翻译：端到端任务导向对话系统通常需要大量训练数据才能取得良好性能。相比之下，基于大语言模型的任务导向对话系统即使数据有限也能表现出色，这得益于其通过上下文示例学习任务的能力。然而，这些模型与训练数据中的回复风格存在偏差，且往往生成过于详尽的回复，导致用户难以快速获取关键信息。为此，我们提出SyncTOD方法，通过大语言模型与任务特定提示的协同优化来提升低数据场景下的对齐效果。SyncTOD采用小型辅助模型生成提示并筛选上下文示例。实验表明，在低数据场景下，SyncTOD结合ChatGPT的性能优于基于大语言模型的基线方法和当前最优模型，同时在完整数据场景下仍保持竞争优势。