用心理学提示引导大模型，融合多模态行为提升人格评估准确率。
Traits Run Deep: Enhancing Personality Assessment via Psychology-Guided LLM Representations and Multimodal Apparent Behaviors
- 用心理学指导的提示词激发大模型提取人格相关语义。
- 跨模态融合网络使异步多模态信号对齐，MSE降低约45%。
- 适合心理评估、个性化教育等需精准人格分析的应用场景。
准确可靠的人格评估在情绪智力、心理健康诊断和个性化教育等领域至关重要。与短暂情绪不同，人格特质是稳定的，常通过语言、面部表情和身体行为无意识地流露，且各模态间呈现异步特征。传统浅层特征难以建模人格语义，跨模态理解也一度被认为难以实现。为此，我们提出名为「Traits Run Deep」的新框架，采用心理学引导的提示词，激发大语言模型生成高层级人格相关语义表征；并设计以文本为中心的特质融合网络（Text-Centric Trait Fusion Network），将丰富文本语义作为锚点，对齐并整合其他模态的异步信号。该模块包含分块投影器降维、跨模态连接器与文本增强器实现有效融合，并采用集成回归头提升数据稀缺下的泛化能力。据我们所知，这是首次将人格特异性提示应用于引导大模型提取人格感知语义。进一步融合音视频外显行为特征显著提升了精度。在AVI验证集上，平均平方误差（MSE）降低约45%。在AVI Challenge 2025测试集上的最终评估确认了方法优势，于人格评估赛道排名第一。源代码将公开于https://github.com/MSA-LMC/TraitsRunDeep。
原文摘要 · Abstract (English)
Accurate and reliable personality assessment plays a vital role in many fields, such as emotional intelligence, mental health diagnostics, and personalized education. Unlike fleeting emotions, personality traits are stable, often subconsciously leaked through language, facial expressions, and body behaviors, with asynchronous patterns across modalities. It was hard to model personality semantics with traditional superficial features and seemed impossible to achieve effective cross-modal understanding. To address these challenges, we propose a novel personality assessment framework called \textit{\textbf{Traits Run Deep}}. It employs \textit{\textbf{psychology-informed prompts}} to elicit high-level personality-relevant semantic representations. Besides, it devises a \textit{\textbf{Text-Centric Trait Fusion Network}} that anchors rich text semantics to align and integrate asynchronous signals from other modalities. To be specific, such fusion module includes a Chunk-Wise Projector to decrease dimensionality, a Cross-Modal Connector and a Text Feature Enhancer for effective modality fusion and an ensemble regression head to improve generalization in data-scarce situations. To our knowledge, we are the first to apply personality-specific prompts to guide large language models (LLMs) in extracting personality-aware semantics for improved representation quality. Furthermore, extracting and fusing audio-visual apparent behavior features further improves the accuracy. Experimental results on the AVI validation set have demonstrated the effectiveness of the proposed components, i.e., approximately a 45\% reduction in mean squared error (MSE). Final evaluations on the test set of the AVI Challenge 2025 confirm our method's superiority, ranking first in the Personality Assessment track. The source code will be made available at https://github.com/MSA-LMC/TraitsRunDeep.
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