@inproceedings{li-etal-2025-chatsop,
title = "{C}hat{SOP}: An {SOP}-Guided {MCTS} Planning Framework for Controllable {LLM} Dialogue Agents",
author = "Li, Zhigen and
Peng, Jianxiang and
Wang, Yanmeng and
Cao, Yong and
Shen, Tianhao and
Zhang, Minghui and
Su, Linxi and
Wu, Shang and
Wu, Yihang and
Wang, YuQian and
Wang, Ye and
Hu, Wei and
Li, Jianfeng and
Wang, Shaojun and
Xiao, Jing and
Xiong, Deyi",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.863/",
doi = "10.18653/v1/2025.acl-long.863",
pages = "17637--17659",
ISBN = "979-8-89176-251-0",
abstract = "Dialogue agents powered by Large Language Models (LLMs) show superior performance in various tasks. Despite the better user understanding and human-like responses, their **lack of controllability** remains a key challenge, often leading to unfocused conversations or task failure. To address this, we introduce Standard Operating Procedure (SOP) to regulate dialogue flow. Specifically, we propose **ChatSOP**, a novel SOP-guided Monte Carlo Tree Search (MCTS) planning framework designed to enhance the controllability of LLM-driven dialogue agents. To enable this, we curate a dataset comprising SOP-annotated multi-scenario dialogues, generated using a semi-automated role-playing system with GPT-4o and validated through strict manual quality control. Additionally, we propose a novel method that integrates Chain of Thought reasoning with supervised fine-tuning for SOP prediction and utilizes SOP-guided Monte Carlo Tree Search for optimal action planning during dialogues. Experimental results demonstrate the effectiveness of our method, such as achieving a 27.95{\%} improvement in action accuracy compared to baseline models based on GPT-3.5 and also showing notable gains for open-source models. Dataset and codes are publicly available."
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<abstract>Dialogue agents powered by Large Language Models (LLMs) show superior performance in various tasks. Despite the better user understanding and human-like responses, their **lack of controllability** remains a key challenge, often leading to unfocused conversations or task failure. To address this, we introduce Standard Operating Procedure (SOP) to regulate dialogue flow. Specifically, we propose **ChatSOP**, a novel SOP-guided Monte Carlo Tree Search (MCTS) planning framework designed to enhance the controllability of LLM-driven dialogue agents. To enable this, we curate a dataset comprising SOP-annotated multi-scenario dialogues, generated using a semi-automated role-playing system with GPT-4o and validated through strict manual quality control. Additionally, we propose a novel method that integrates Chain of Thought reasoning with supervised fine-tuning for SOP prediction and utilizes SOP-guided Monte Carlo Tree Search for optimal action planning during dialogues. Experimental results demonstrate the effectiveness of our method, such as achieving a 27.95% improvement in action accuracy compared to baseline models based on GPT-3.5 and also showing notable gains for open-source models. Dataset and codes are publicly available.</abstract>
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%0 Conference Proceedings
%T ChatSOP: An SOP-Guided MCTS Planning Framework for Controllable LLM Dialogue Agents
%A Li, Zhigen
%A Peng, Jianxiang
%A Wang, Yanmeng
%A Cao, Yong
%A Shen, Tianhao
%A Zhang, Minghui
%A Su, Linxi
%A Wu, Shang
%A Wu, Yihang
%A Wang, YuQian
%A Wang, Ye
%A Hu, Wei
%A Li, Jianfeng
%A Wang, Shaojun
%A Xiao, Jing
%A Xiong, Deyi
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F li-etal-2025-chatsop
%X Dialogue agents powered by Large Language Models (LLMs) show superior performance in various tasks. Despite the better user understanding and human-like responses, their **lack of controllability** remains a key challenge, often leading to unfocused conversations or task failure. To address this, we introduce Standard Operating Procedure (SOP) to regulate dialogue flow. Specifically, we propose **ChatSOP**, a novel SOP-guided Monte Carlo Tree Search (MCTS) planning framework designed to enhance the controllability of LLM-driven dialogue agents. To enable this, we curate a dataset comprising SOP-annotated multi-scenario dialogues, generated using a semi-automated role-playing system with GPT-4o and validated through strict manual quality control. Additionally, we propose a novel method that integrates Chain of Thought reasoning with supervised fine-tuning for SOP prediction and utilizes SOP-guided Monte Carlo Tree Search for optimal action planning during dialogues. Experimental results demonstrate the effectiveness of our method, such as achieving a 27.95% improvement in action accuracy compared to baseline models based on GPT-3.5 and also showing notable gains for open-source models. Dataset and codes are publicly available.
%R 10.18653/v1/2025.acl-long.863
%U https://aclanthology.org/2025.acl-long.863/
%U https://doi.org/10.18653/v1/2025.acl-long.863
%P 17637-17659
Markdown (Informal)
[ChatSOP: An SOP-Guided MCTS Planning Framework for Controllable LLM Dialogue Agents](https://aclanthology.org/2025.acl-long.863/) (Li et al., ACL 2025)
ACL
- Zhigen Li, Jianxiang Peng, Yanmeng Wang, Yong Cao, Tianhao Shen, Minghui Zhang, Linxi Su, Shang Wu, Yihang Wu, YuQian Wang, Ye Wang, Wei Hu, Jianfeng Li, Shaojun Wang, Jing Xiao, and Deyi Xiong. 2025. ChatSOP: An SOP-Guided MCTS Planning Framework for Controllable LLM Dialogue Agents. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 17637–17659, Vienna, Austria. Association for Computational Linguistics.