  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.
