Detecting Conversational Mental Manipulation with Intent-Aware Prompting
This addresses mental health care by improving detection of covert manipulation in conversations, though it appears incremental as it builds on existing prompting strategies.
The paper tackles the problem of detecting subtle mental manipulation in conversations by proposing Intent-Aware Prompting (IAP), which uses large language models to capture underlying intents, resulting in superior effectiveness on the MentalManip dataset and a substantial reduction in false negatives.
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.