Large language models (LLM) based end-to-end task-oriented dialog (TOD) systems built using few-shot (in-context) learning perform better than supervised models only when the train data is limited. This is due to the inherent ability of LLMs to learn any task with just a few demonstrations. As the number of train dialogs increases, supervised SoTA models surpass in-context learning LLMs as they learn to better align with the style of the system responses in the training data, which LLMs struggle to mimic. In response, we propose SyncTOD, which synergizes LLMs with useful hints about the task for improved alignment. At a high level, SyncTOD trains auxiliary models to provide these hints and select exemplars for the 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：基于大语言模型（LLM）采用少样本（上下文）学习构建的端到端任务导向对话系统，仅在训练数据有限时表现优于监督模型。这得益于LLM仅需少量示例即可学习任意任务的固有能力。随着训练对话数量的增加，监督学习的当前最优模型会超越上下文学习的LLM，因为它们能更好地学习训练数据中系统回复的风格特征，而LLM难以准确模仿这种风格。为此，我们提出SyncTOD方法，通过向LLM提供任务相关提示信息以增强对齐能力。SyncTOD通过训练辅助模型来生成提示信息并筛选上下文提示中的示例样本。实验表明，在低数据场景下，基于ChatGPT的SyncTOD相较于LLM基线模型和当前最优模型具有显著优势，同时在完整数据场景下仍保持竞争力。