@inproceedings{zhang-etal-2025-pfdial,
title = "{PFD}ial: A Structured Dialogue Instruction Fine-tuning Method Based on {UML} Flowcharts",
author = "Zhang, Ming and
Wang, Yuhui and
Shen, Yujiong and
Yang, Tingyi and
Jiang, Changhao and
Wu, Yilong and
Dou, Shihan and
Chen, Qinhao and
Xi, Zhiheng and
Zhang, Zhihao and
Dong, Yi and
Wang, Zhen and
Fei, Zhihui and
Wan, Mingyang and
Liang, Tao and
Ma, Guojun and
Zhang, Qi and
Gui, Tao and
Huang, Xuanjing",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.134/",
doi = "10.18653/v1/2025.findings-acl.134",
pages = "2626--2649",
ISBN = "979-8-89176-256-5",
abstract = "Process-driven dialogue systems, which operate under strict predefined process constraints, are essential in customer service and equipment maintenance scenarios. Although Large Language Models (LLMs) have shown remarkable progress in dialogue and reasoning, they still struggle to solve these strictly constrained dialogue tasks. To address this challenge, we construct \textbf{P}rocess \textbf{F}low \textbf{Dial}ogue (\textbf{PFDial}) dataset, which contains 12,705 high-quality Chinese dialogue instructions derived from 440 flowcharts containing 5,055 process nodes. Based on PlantUML specification, each UML flowchart is converted into atomic dialogue units i.e., structured five-tuples. Experimental results demonstrate that a 7B model trained with merely 800 samples, and a 0.5B model trained on total data both can surpass 90{\%} accuracy. Additionally, the 8B model can surpass GPT-4o up to 43.88{\%} with an average of 11.00{\%}. We further evaluate models' performance on challenging backward transitions in process flows and conduct an in-depth analysis of various dataset formats to reveal their impact on model performance in handling decision and sequential branches. The data is released in \url{https://github.com/KongLongGeFDU/PFDial}."
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<abstract>Process-driven dialogue systems, which operate under strict predefined process constraints, are essential in customer service and equipment maintenance scenarios. Although Large Language Models (LLMs) have shown remarkable progress in dialogue and reasoning, they still struggle to solve these strictly constrained dialogue tasks. To address this challenge, we construct Process Flow Dialogue (PFDial) dataset, which contains 12,705 high-quality Chinese dialogue instructions derived from 440 flowcharts containing 5,055 process nodes. Based on PlantUML specification, each UML flowchart is converted into atomic dialogue units i.e., structured five-tuples. Experimental results demonstrate that a 7B model trained with merely 800 samples, and a 0.5B model trained on total data both can surpass 90% accuracy. Additionally, the 8B model can surpass GPT-4o up to 43.88% with an average of 11.00%. We further evaluate models’ performance on challenging backward transitions in process flows and conduct an in-depth analysis of various dataset formats to reveal their impact on model performance in handling decision and sequential branches. The data is released in https://github.com/KongLongGeFDU/PFDial.</abstract>
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%0 Conference Proceedings
%T PFDial: A Structured Dialogue Instruction Fine-tuning Method Based on UML Flowcharts
%A Zhang, Ming
%A Wang, Yuhui
%A Shen, Yujiong
%A Yang, Tingyi
%A Jiang, Changhao
%A Wu, Yilong
%A Dou, Shihan
%A Chen, Qinhao
%A Xi, Zhiheng
%A Zhang, Zhihao
%A Dong, Yi
%A Wang, Zhen
%A Fei, Zhihui
%A Wan, Mingyang
%A Liang, Tao
%A Ma, Guojun
%A Zhang, Qi
%A Gui, Tao
%A Huang, Xuanjing
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F zhang-etal-2025-pfdial
%X Process-driven dialogue systems, which operate under strict predefined process constraints, are essential in customer service and equipment maintenance scenarios. Although Large Language Models (LLMs) have shown remarkable progress in dialogue and reasoning, they still struggle to solve these strictly constrained dialogue tasks. To address this challenge, we construct Process Flow Dialogue (PFDial) dataset, which contains 12,705 high-quality Chinese dialogue instructions derived from 440 flowcharts containing 5,055 process nodes. Based on PlantUML specification, each UML flowchart is converted into atomic dialogue units i.e., structured five-tuples. Experimental results demonstrate that a 7B model trained with merely 800 samples, and a 0.5B model trained on total data both can surpass 90% accuracy. Additionally, the 8B model can surpass GPT-4o up to 43.88% with an average of 11.00%. We further evaluate models’ performance on challenging backward transitions in process flows and conduct an in-depth analysis of various dataset formats to reveal their impact on model performance in handling decision and sequential branches. The data is released in https://github.com/KongLongGeFDU/PFDial.
%R 10.18653/v1/2025.findings-acl.134
%U https://aclanthology.org/2025.findings-acl.134/
%U https://doi.org/10.18653/v1/2025.findings-acl.134
%P 2626-2649
Markdown (Informal)
[PFDial: A Structured Dialogue Instruction Fine-tuning Method Based on UML Flowcharts](https://aclanthology.org/2025.findings-acl.134/) (Zhang et al., Findings 2025)
ACL
- Ming Zhang, Yuhui Wang, Yujiong Shen, Tingyi Yang, Changhao Jiang, Yilong Wu, Shihan Dou, Qinhao Chen, Zhiheng Xi, Zhihao Zhang, Yi Dong, Zhen Wang, Zhihui Fei, Mingyang Wan, Tao Liang, Guojun Ma, Qi Zhang, Tao Gui, and Xuanjing Huang. 2025. PFDial: A Structured Dialogue Instruction Fine-tuning Method Based on UML Flowcharts. In Findings of the Association for Computational Linguistics: ACL 2025, pages 2626–2649, Vienna, Austria. Association for Computational Linguistics.