  Task-oriented dialogue (TOD) systems aim to efficiently handle task-oriented
conversations, including information collection. How to utilize TOD accurately,
efficiently and effectively for information collection has always been a
critical and challenging task. Recent studies have demonstrated that Large
Language Models (LLMs) excel in dialogue, instruction generation, and
reasoning, and can significantly enhance the performance of TOD through
fine-tuning. However, current datasets primarily cater to user-led systems and
are limited to predefined specific scenarios and slots, thereby necessitating
improvements in the proactiveness, diversity, and capabilities of TOD. In this
study, we present a detailed multi-domain task-oriented data construction
process for conversations, and a Chinese dialogue dataset generated based on
this process, TransferTOD, which authentically simulates human-computer
dialogues in 30 popular life service scenarios. Leveraging this dataset, we
trained a model called TransferTOD-7B using full-parameter fine-tuning,
showcasing notable abilities in slot filling and questioning. Our work has
demonstrated its strong generalization capabilities in various downstream
scenarios, significantly enhancing both data utilization efficiency and system
performance. The data is released in
https://github.com/KongLongGeFDU/TransferTOD.
