Language-based personality assessment from life narratives: a focus on model interpretability and efficiency

Natural Language Processing (NLP) enables novel approaches to personality assessment by analyzing rich, open-ended narratives, rather than relying solely on structured questionnaires. While most existing work focuses on short-form social media texts, this study shifts attention to predicting the Big Five personality traits, a framework closely tied to mental-health outcomes, from long-form life narrative interviews. Each narrative exceeds 2,000 words, posing significant challenges for standard language models. We propose a two-step modeling framework that captures contextual representations of long texts while prioritizing interpretability and computational efficiency. First, we extract contextual embeddings using a sliding-window finetuning strategy on pretrained transformer models. These embeddings are then processed with Recurrent Neural Networks (RNNs) equipped with attention mechanisms to capture long-range dependencies and provide interpretable outputs. Our hybrid approach effectively combines the representational power of transformers with the sequence-handling strengths of RNNs. Through comparisons with state-of-the-art long-context models and interpretability analyses, we demonstrate improvements in prediction accuracy, computational efficiency, and interpretability. These findings underscore the potential of interpretable and efficient language models for assessing personality from life narratives.