通过自我觉察框架提升大模型对多人对话中心理操控的识别能力
SELF-PERCEPT: Introspection Improves Large Language Models' Detection of Multi-Person Mental Manipulation in Conversations
- 基于自我觉察理论设计两阶段提示框架，增强模型自省能力
- 在220段真人秀对话上测试，显著提升对11种心理操控的检测准确率
- 适合安全防护、社交行为分析等需识别隐性操纵的应用场景
心理操控是人际沟通中一种微妙但普遍的虐待形式，其检测对保护潜在受害者至关重要。由于操控行为具有高度情境性和细微差别，大语言模型（LLMs）在复杂、多轮、多人对话中识别操纵性语言仍面临巨大挑战。为此，我们构建了MultiManip数据集，包含220段多轮、多人对话，平衡了操纵与非操纵交互，均来自模仿真实情境的真人秀节目。操纵类对话涵盖11种真实生活中的操控类型。我们对GPT-4o和Llama-3.1-8B等前沿LLM进行了广泛评估，采用多种提示策略。尽管模型能力强大，但仍难以有效识别操控。为此，我们提出SELF-PERCEPT——一种受自我觉察理论启发的新型两阶段提示框架，在多人群体、多轮对话的心理操控检测任务中表现优异。代码与数据已公开于https://github.com/danushkhanna/self-percept。
原文摘要 · Abstract (English)
Mental manipulation is a subtle yet pervasive form of abuse in interpersonal communication, making its detection critical for safeguarding potential victims. However, due to manipulation's nuanced and context-specific nature, identifying manipulative language in complex, multi-turn, and multi-person conversations remains a significant challenge for large language models (LLMs). To address this gap, we introduce the MultiManip dataset, comprising 220 multi-turn, multi-person dialogues balanced between manipulative and non-manipulative interactions, all drawn from reality shows that mimic real-world scenarios. For manipulative interactions, it includes 11 distinct manipulations depicting real-life scenarios. We conduct extensive evaluations of state-of-the-art LLMs, such as GPT-4o and Llama-3.1-8B, employing various prompting strategies. Despite their capabilities, these models often struggle to detect manipulation effectively. To overcome this limitation, we propose SELF-PERCEPT, a novel, two-stage prompting framework inspired by Self-Perception Theory, demonstrating strong performance in detecting multi-person, multi-turn mental manipulation. Our code and data are publicly available at https://github.com/danushkhanna/self-percept .
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