@inproceedings{kim-etal-2025-principles,
title = "{PRINCIPLES}: Synthetic Strategy Memory for Proactive Dialogue Agents",
author = "Kim, Namyoung and
Ong, Kai Tzu-iunn and
Hwang, Yeonjun and
Kang, Minseok and
Jihn, Iiseo and
Kim, Gayoung and
Kim, Minju and
Yeo, Jinyoung",
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.1164/",
pages = "21329--21368",
ISBN = "979-8-89176-335-7",
abstract = "Dialogue agents based on large language models (LLMs) have shown promising performance in proactive dialogue, which requires effective strategy planning. However, existing approaches to strategy planning for proactive dialogue face several limitations: limited strategy coverage, preference bias in planning, and reliance on costly additional training. To address these, we propose PRINCIPLES: a synthetic strategy memory for proactive dialogue agents. PRINCIPLES is derived through offline self-play simulations and serves as reusable knowledge that guides strategy planning during inference, eliminating the need for additional training and data annotation. We evaluate PRINCIPLES in both emotional support and persuasion domains, demonstrating consistent improvements over strong baselines. Furthermore, PRINCIPLES maintains its robustness across extended and more diverse evaluation settings. See our project page at https://huggingface.co/spaces/kimnamssya/Principles."
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<abstract>Dialogue agents based on large language models (LLMs) have shown promising performance in proactive dialogue, which requires effective strategy planning. However, existing approaches to strategy planning for proactive dialogue face several limitations: limited strategy coverage, preference bias in planning, and reliance on costly additional training. To address these, we propose PRINCIPLES: a synthetic strategy memory for proactive dialogue agents. PRINCIPLES is derived through offline self-play simulations and serves as reusable knowledge that guides strategy planning during inference, eliminating the need for additional training and data annotation. We evaluate PRINCIPLES in both emotional support and persuasion domains, demonstrating consistent improvements over strong baselines. Furthermore, PRINCIPLES maintains its robustness across extended and more diverse evaluation settings. See our project page at https://huggingface.co/spaces/kimnamssya/Principles.</abstract>
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%0 Conference Proceedings
%T PRINCIPLES: Synthetic Strategy Memory for Proactive Dialogue Agents
%A Kim, Namyoung
%A Ong, Kai Tzu-iunn
%A Hwang, Yeonjun
%A Kang, Minseok
%A Jihn, Iiseo
%A Kim, Gayoung
%A Kim, Minju
%A Yeo, Jinyoung
%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 kim-etal-2025-principles
%X Dialogue agents based on large language models (LLMs) have shown promising performance in proactive dialogue, which requires effective strategy planning. However, existing approaches to strategy planning for proactive dialogue face several limitations: limited strategy coverage, preference bias in planning, and reliance on costly additional training. To address these, we propose PRINCIPLES: a synthetic strategy memory for proactive dialogue agents. PRINCIPLES is derived through offline self-play simulations and serves as reusable knowledge that guides strategy planning during inference, eliminating the need for additional training and data annotation. We evaluate PRINCIPLES in both emotional support and persuasion domains, demonstrating consistent improvements over strong baselines. Furthermore, PRINCIPLES maintains its robustness across extended and more diverse evaluation settings. See our project page at https://huggingface.co/spaces/kimnamssya/Principles.
%U https://aclanthology.org/2025.findings-emnlp.1164/
%P 21329-21368
Markdown (Informal)
[PRINCIPLES: Synthetic Strategy Memory for Proactive Dialogue Agents](https://aclanthology.org/2025.findings-emnlp.1164/) (Kim et al., Findings 2025)
ACL
- Namyoung Kim, Kai Tzu-iunn Ong, Yeonjun Hwang, Minseok Kang, Iiseo Jihn, Gayoung Kim, Minju Kim, and Jinyoung Yeo. 2025. PRINCIPLES: Synthetic Strategy Memory for Proactive Dialogue Agents. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 21329–21368, Suzhou, China. Association for Computational Linguistics.