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    <abstract>Mental manipulation severely undermines mental wellness by covertly and negatively distorting decision-making. While there is an increasing interest in mental health care within the natural language processing community, progress in tackling manipulation remains limited due to the complexity of detecting subtle, covert tactics in conversations. In this paper, we propose Intent-Aware Prompting (IAP), a novel approach for detecting mental manipulations using large language models (LLMs), providing a deeper understanding of manipulative tactics by capturing the underlying intents of participants. Experimental results on the MentalManip dataset demonstrate superior effectiveness of IAP against other advanced prompting strategies. Notably, our approach substantially reduces false negatives, helping detect more instances of mental manipulation with minimal misjudgment of positive cases. The code of this paper is available at https://github.com/Anton-Jiayuan-MA/Manip-IAP.</abstract>
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%0 Conference Proceedings
%T Detecting Conversational Mental Manipulation with Intent-Aware Prompting
%A Ma, Jiayuan
%A Na, Hongbin
%A Wang, Zimu
%A Hua, Yining
%A Liu, Yue
%A Wang, Wei
%A Chen, Ling
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F ma-etal-2025-detecting
%X Mental manipulation severely undermines mental wellness by covertly and negatively distorting decision-making. While there is an increasing interest in mental health care within the natural language processing community, progress in tackling manipulation remains limited due to the complexity of detecting subtle, covert tactics in conversations. In this paper, we propose Intent-Aware Prompting (IAP), a novel approach for detecting mental manipulations using large language models (LLMs), providing a deeper understanding of manipulative tactics by capturing the underlying intents of participants. Experimental results on the MentalManip dataset demonstrate superior effectiveness of IAP against other advanced prompting strategies. Notably, our approach substantially reduces false negatives, helping detect more instances of mental manipulation with minimal misjudgment of positive cases. The code of this paper is available at https://github.com/Anton-Jiayuan-MA/Manip-IAP.
%U https://aclanthology.org/2025.coling-main.616/
%P 9176-9183

Markdown (Informal)

[Detecting Conversational Mental Manipulation with Intent-Aware Prompting](https://aclanthology.org/2025.coling-main.616/) (Ma et al., COLING 2025)

• Detecting Conversational Mental Manipulation with Intent-Aware Prompting (Ma et al., COLING 2025)

ACL

• Jiayuan Ma, Hongbin Na, Zimu Wang, Yining Hua, Yue Liu, Wei Wang, and Ling Chen. 2025. Detecting Conversational Mental Manipulation with Intent-Aware Prompting. In Proceedings of the 31st International Conference on Computational Linguistics, pages 9176–9183, Abu Dhabi, UAE. Association for Computational Linguistics.