特质深藏：通过心理学引导的大语言模型表征与多模态外显行为增强人格评估
Traits Run Deep: Enhancing Personality Assessment via Psychology-Guided LLM Representations and Multimodal Apparent Behaviors
AI总结：
针对传统人格评估难以建模人格语义、跨模态理解效果差的问题，提出Traits Run Deep框架，用心理学引导的LLM提取人格语义，结合文本中心多模态融合网络，在2025 AVI挑战赛人格评估赛道夺冠。
摘要：
准确可靠的人格评估在情商、心理健康诊断、个性化教育等诸多领域发挥着重要作用。与转瞬即逝的情绪不同，人格特质是稳定的，往往会通过语言、面部表情和肢体行为在潜意识中流露，且在不同模态间呈现异步模式。传统的浅层特征难以对人格语义进行建模，实现有效的跨模态理解似乎也无法做到。为应对这些挑战，我们提出了一种名为**Traits Run Deep**的新型人格评估框架。该框架采用**心理学启发提示（psychology-informed prompts）**来提取高层的人格相关语义表征。此外，它还设计了**文本中心特质融合网络（Text-Centric Trait Fusion Network）**，以丰富的文本语义为锚点，对齐并整合来自其他模态的异步信号。具体而言，该融合模块包含用于降维的分块投影器（Chunk-Wise Projector）、用于有效模态融合的跨模态连接器（Cross-Modal Connector）和文本特征增强器（Text Feature Enhancer），以及用于提升数据稀缺场景下泛化能力的集成回归头。据我们所知，我们是首个应用人格专属提示引导大语言模型（LLM）提取具备人格感知的语义、以提升表征质量的研究。此外，提取并融合音视频外显行为特征进一步提升了准确率。在AVI验证集上的实验结果证明了所提组件的有效性，即均方误差（MSE）降低了约45%。在2025年AVI挑战赛测试集上的最终评估证实了我们方法的优越性，在人格评估赛道排名第一。源代码将发布于https://github.com/MSA-LMC/TraitsRunDeep。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
