@inproceedings{zhao-etal-2025-chain,
title = "Chain of Strategy Optimization Makes Large Language Models Better Emotional Supporter",
author = "Zhao, Weixiang and
Sui, Xingyu and
Han, Xinyang and
Deng, Yang and
Hu, Yulin and
Guo, Jiahe and
Qin, Libo and
Du, Qianyun and
Wang, Shijin and
Zhao, Yanyan and
Qin, Bing and
Liu, Ting",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.831/",
doi = "10.18653/v1/2025.findings-emnlp.831",
pages = "15361--15381",
ISBN = "979-8-89176-335-7",
abstract = "The growing emotional stress in modern society has increased the demand for Emotional Support Conversations (ESC). While Large Language Models (LLMs) show promise for ESC, they face two key challenges: (1) low strategy selection accuracy, and (2) preference bias, limiting their adaptability to users' emotional needs. Existing supervised fine-tuning (SFT) struggles to address these issues, as it rigidly trains models on single gold-standard responses without modeling nuanced strategy trade-offs. To overcome these limitations, we propose a novel two-stage framework that optimizes strategy selection preferences at each dialogue turn. We first leverage Monte Carlo Tree Search to construct ESC-Pro, a high-quality preference dataset with turn-level strategy-response pairs. Then training on ESC-Pro with Chain-of-Strategy Optimization (CSO) improves both strategy accuracy and bias mitigation, enabling LLMs to generate more empathetic and contextually appropriate responses. Experiments on LLaMA-3.1-8B, Gemma-2-9B, and Qwen2.5-7B demonstrate that CSO outperforms standard SFT, highlighting the efficacy of fine-grained, turn-level preference modeling in ESC."
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<abstract>The growing emotional stress in modern society has increased the demand for Emotional Support Conversations (ESC). While Large Language Models (LLMs) show promise for ESC, they face two key challenges: (1) low strategy selection accuracy, and (2) preference bias, limiting their adaptability to users’ emotional needs. Existing supervised fine-tuning (SFT) struggles to address these issues, as it rigidly trains models on single gold-standard responses without modeling nuanced strategy trade-offs. To overcome these limitations, we propose a novel two-stage framework that optimizes strategy selection preferences at each dialogue turn. We first leverage Monte Carlo Tree Search to construct ESC-Pro, a high-quality preference dataset with turn-level strategy-response pairs. Then training on ESC-Pro with Chain-of-Strategy Optimization (CSO) improves both strategy accuracy and bias mitigation, enabling LLMs to generate more empathetic and contextually appropriate responses. Experiments on LLaMA-3.1-8B, Gemma-2-9B, and Qwen2.5-7B demonstrate that CSO outperforms standard SFT, highlighting the efficacy of fine-grained, turn-level preference modeling in ESC.</abstract>
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%0 Conference Proceedings
%T Chain of Strategy Optimization Makes Large Language Models Better Emotional Supporter
%A Zhao, Weixiang
%A Sui, Xingyu
%A Han, Xinyang
%A Deng, Yang
%A Hu, Yulin
%A Guo, Jiahe
%A Qin, Libo
%A Du, Qianyun
%A Wang, Shijin
%A Zhao, Yanyan
%A Qin, Bing
%A Liu, Ting
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F zhao-etal-2025-chain
%X The growing emotional stress in modern society has increased the demand for Emotional Support Conversations (ESC). While Large Language Models (LLMs) show promise for ESC, they face two key challenges: (1) low strategy selection accuracy, and (2) preference bias, limiting their adaptability to users’ emotional needs. Existing supervised fine-tuning (SFT) struggles to address these issues, as it rigidly trains models on single gold-standard responses without modeling nuanced strategy trade-offs. To overcome these limitations, we propose a novel two-stage framework that optimizes strategy selection preferences at each dialogue turn. We first leverage Monte Carlo Tree Search to construct ESC-Pro, a high-quality preference dataset with turn-level strategy-response pairs. Then training on ESC-Pro with Chain-of-Strategy Optimization (CSO) improves both strategy accuracy and bias mitigation, enabling LLMs to generate more empathetic and contextually appropriate responses. Experiments on LLaMA-3.1-8B, Gemma-2-9B, and Qwen2.5-7B demonstrate that CSO outperforms standard SFT, highlighting the efficacy of fine-grained, turn-level preference modeling in ESC.
%R 10.18653/v1/2025.findings-emnlp.831
%U https://aclanthology.org/2025.findings-emnlp.831/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.831
%P 15361-15381
Markdown (Informal)
[Chain of Strategy Optimization Makes Large Language Models Better Emotional Supporter](https://aclanthology.org/2025.findings-emnlp.831/) (Zhao et al., Findings 2025)
ACL
- Weixiang Zhao, Xingyu Sui, Xinyang Han, Yang Deng, Yulin Hu, Jiahe Guo, Libo Qin, Qianyun Du, Shijin Wang, Yanyan Zhao, Bing Qin, and Ting Liu. 2025. Chain of Strategy Optimization Makes Large Language Models Better Emotional Supporter. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 15361–15381, Suzhou, China. Association for Computational Linguistics.