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.

翻译：心理操控通过隐蔽且消极地扭曲决策过程，严重损害心理健康。尽管自然语言处理领域对心理健康护理的关注日益增加，但由于检测对话中微妙、隐蔽策略的复杂性，应对操控方面的进展仍然有限。本文提出意图感知提示（IAP），一种利用大语言模型（LLMs）检测心理操控的新方法，通过捕捉参与者的潜在意图，提供对操控策略的更深入理解。在MentalManip数据集上的实验结果表明，IAP相较于其他先进提示策略具有显著优势。值得注意的是，我们的方法大幅减少了假阴性，有助于在最小化阳性案例误判的情况下检测更多心理操控实例。本文代码发布于 https://github.com/Anton-Jiayuan-MA/Manip-IAP。