基于语言模型的人生故事人格预测
Personality Prediction from Life Stories using Language Models
AI总结：
本研究提出一种结合滑动窗口微调预训练语言模型与注意力循环神经网络的两步方法，用于从超过2000词元的长篇生活叙事访谈中预测五因素人格特质，相比LLaMA和Longformer等模型在准确性、效率和可解释性上均有提升。
摘要：
自然语言处理（NLP）通过利用丰富的开放式文本，为人格评估提供了新途径，超越了传统的问卷调查。在本研究中，我们解决了对每篇超过2000个词元的长篇叙事访谈进行建模的挑战，以预测五因素模型（FFM）的人格特质。我们提出了一种两步方法：首先，我们使用预训练语言模型的滑动窗口微调来提取上下文嵌入；然后，我们应用带有注意力机制的循环神经网络（RNN）来整合长距离依赖并增强可解释性。这种混合方法有效地结合了预训练Transformer和序列建模的优势，以处理长上下文数据。通过消融研究以及与LLaMA和Longformer等最先进的长上下文模型的比较，我们展示了在预测准确性、效率和可解释性方面的改进。我们的结果凸显了将基于语言的特征与长上下文建模相结合，以推进基于生活叙事的人格评估的潜力。
英文摘要：
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.
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。
