Detecting Conversational Mental Manipulation with Intent-Aware Prompting

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.

Accepted at COLING2025. Oral Presentation. Best Short Paper Award. For code and data, see this https URL

Computation and Language (cs.CL)

arXiv:2412.08414 [cs.CL]

(or arXiv:2412.08414v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2412.08414 Focus to learn more arXiv-issued DOI via DataCite

Proceedings of the 31st International Conference on Computational Linguistics, pp. 9176-9183, 2025

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