Personality Prediction from Life Stories using Language Models

TL;DR

This paper presents a hybrid NLP approach combining pretrained language models and RNNs with attention to predict personality traits from long life stories, improving accuracy and interpretability over existing methods.

Contribution

It introduces a novel two-step method for modeling long narrative texts for personality prediction, integrating sliding-window embeddings with attention-based RNNs.

Findings

Improved prediction accuracy over state-of-the-art models.

Enhanced interpretability of personality predictions.

Efficient handling of long narrative texts.

Abstract

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.