用大模型分析人生故事，精准预测五大性格特质。
Personality Prediction from Life Stories using Language Models
- 分段提取文本嵌入，再用带注意力的RNN整合长文本依赖
- 在超2000词的叙事文本上，准确率优于现有长文本模型
- 兼顾效果与可解释性，适合心理评估与人机交互研究
自然语言处理为性格评估提供了新路径，通过分析丰富的开放文本，超越传统问卷。本研究聚焦于处理超过2000个标记（tokens）的长篇叙述访谈，以预测五因素模型（Five-Factor Model, FFM）人格特质。提出两步法：首先利用滑动窗口微调预训练语言模型提取上下文嵌入；随后采用带注意力机制的循环神经网络（RNN）建模长程依赖并提升可解释性。该混合方法有效结合了预训练变换器与序列建模的优势，成功处理长文本。通过消融实验及与LLaMA、Longformer等前沿长文本模型对比，验证了在预测精度、效率和可解释性上的提升。结果表明，融合语言特征与长文本建模，能显著推进基于人生叙事的性格评估。
原文摘要 · Abstract (English)
Natural Language Processing (NLP) offers new avenues for personality assessment by leveraging rich, open-ended text, moving beyond traditional questionnaires. In this study, we address the challenge of modeling long narrative interview where each exceeds 2000 tokens so as to predict Five-Factor Model (FFM) personality traits. We propose a two-step approach: first, we extract contextual embeddings using sliding-window fine-tuning of pretrained language models; then, we apply Recurrent Neural Networks (RNNs) with attention mechanisms to integrate long-range dependencies and enhance interpretability. This hybrid method effectively bridges the strengths of pretrained transformers and sequence modeling to handle long-context data. Through ablation studies and comparisons with state-of-the-art long-context models such as LLaMA and Longformer, we demonstrate improvements in prediction accuracy, efficiency, and interpretability. Our results highlight the potential of combining language-based features with long-context modeling to advance personality assessment from life narratives.
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