@inproceedings{ma-etal-2025-enhancing,
title = "Enhancing {LLM}-Based Persuasion Simulations with Cultural and Speaker-Specific Information",
author = "Ma, Weicheng and
Zhang, Hefan and
Ji, Shiyu and
Hashemi, Farnoosh and
Wang, Qichao and
Yang, Ivory and
Chen, Joice and
Pan, Juanwen and
Macy, Michael and
Hassanpour, Saeed and
Vosoughi, Soroush",
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.808/",
pages = "14955--14976",
ISBN = "979-8-89176-335-7",
abstract = "Large language models (LLMs) have been used to synthesize persuasive dialogues for studying persuasive behavior. However, existing approaches often suffer from issues such as stance oscillation and low informativeness. To address these challenges, we propose reinforced instructional prompting, a method that ensures speaker characteristics consistently guide all stages of dialogue generation. We further introduce multilingual prompting, which aligns language use with speakers' native languages to better capture cultural nuances. Our experiments involving speakers from eight countries show that continually reinforcing speaker profiles and cultural context improves argument diversity, enhances informativeness, and stabilizes speaker stances. Moreover, our analysis of inter-group versus intra-group persuasion reveals that speakers engaging within their own cultural groups employ more varied persuasive strategies than in cross-cultural interactions. These findings underscore the importance of speaker and cultural awareness in LLM-based persuasion modeling and suggest new directions for developing more personalized, ethically grounded, and culturally adaptive LLM-generated dialogues."
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<abstract>Large language models (LLMs) have been used to synthesize persuasive dialogues for studying persuasive behavior. However, existing approaches often suffer from issues such as stance oscillation and low informativeness. To address these challenges, we propose reinforced instructional prompting, a method that ensures speaker characteristics consistently guide all stages of dialogue generation. We further introduce multilingual prompting, which aligns language use with speakers’ native languages to better capture cultural nuances. Our experiments involving speakers from eight countries show that continually reinforcing speaker profiles and cultural context improves argument diversity, enhances informativeness, and stabilizes speaker stances. Moreover, our analysis of inter-group versus intra-group persuasion reveals that speakers engaging within their own cultural groups employ more varied persuasive strategies than in cross-cultural interactions. These findings underscore the importance of speaker and cultural awareness in LLM-based persuasion modeling and suggest new directions for developing more personalized, ethically grounded, and culturally adaptive LLM-generated dialogues.</abstract>
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%0 Conference Proceedings
%T Enhancing LLM-Based Persuasion Simulations with Cultural and Speaker-Specific Information
%A Ma, Weicheng
%A Zhang, Hefan
%A Ji, Shiyu
%A Hashemi, Farnoosh
%A Wang, Qichao
%A Yang, Ivory
%A Chen, Joice
%A Pan, Juanwen
%A Macy, Michael
%A Hassanpour, Saeed
%A Vosoughi, Soroush
%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 ma-etal-2025-enhancing
%X Large language models (LLMs) have been used to synthesize persuasive dialogues for studying persuasive behavior. However, existing approaches often suffer from issues such as stance oscillation and low informativeness. To address these challenges, we propose reinforced instructional prompting, a method that ensures speaker characteristics consistently guide all stages of dialogue generation. We further introduce multilingual prompting, which aligns language use with speakers’ native languages to better capture cultural nuances. Our experiments involving speakers from eight countries show that continually reinforcing speaker profiles and cultural context improves argument diversity, enhances informativeness, and stabilizes speaker stances. Moreover, our analysis of inter-group versus intra-group persuasion reveals that speakers engaging within their own cultural groups employ more varied persuasive strategies than in cross-cultural interactions. These findings underscore the importance of speaker and cultural awareness in LLM-based persuasion modeling and suggest new directions for developing more personalized, ethically grounded, and culturally adaptive LLM-generated dialogues.
%U https://aclanthology.org/2025.findings-emnlp.808/
%P 14955-14976
Markdown (Informal)
[Enhancing LLM-Based Persuasion Simulations with Cultural and Speaker-Specific Information](https://aclanthology.org/2025.findings-emnlp.808/) (Ma et al., Findings 2025)
ACL
- Weicheng Ma, Hefan Zhang, Shiyu Ji, Farnoosh Hashemi, Qichao Wang, Ivory Yang, Joice Chen, Juanwen Pan, Michael Macy, Saeed Hassanpour, and Soroush Vosoughi. 2025. Enhancing LLM-Based Persuasion Simulations with Cultural and Speaker-Specific Information. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 14955–14976, Suzhou, China. Association for Computational Linguistics.