ExPAM: Explainable personality assessment method using heterogeneous linguistic features and off-the-shelf LLMs
Abstract
Many organizations are increasingly adopting personalization techniques to enhance a user satisfaction. However, current systems generally lack the ability to automatically infer and interpret individual Personality traits (PTs), which are key drivers of user behavior. Large Language Models (LLMs) are widely used, but they are still not well-suited to reliable and explainable Personality Assessment (PA). To address this gap, we propose ExPAM, a novel Explainable Personality Assessment Method that leverages hybrid feature fusion and in-context learning with off-the-shelf Large Language Models (LLMs) to predict Big Five PTs from textual data. It allows explicitly grounding predictions in interpretable linguistic patterns without requiring Large Language Models (LLMs) fine-tuning. The hybrid fusion is designed to simultaneously enhance predictive performance and model interpretability in Personality Assessment (PA). Specifically, transformer-based embeddings encode local contextual information, while features extracted via the Linguistic Inquiry and Word Count (LIWC) dictionary provide complementary global and local linguistic indicators of PTs. These interpretable feature patterns are incorporated into prompts that guide the LLM to generate both PTs predictions and human-understandable explanations. Evaluated on the ChaLearn First Impressions v2 corpus, ExPAM outperforms models relying on either feature type alone, achieving a mean accuracy (mACC) of 0.891 and a Concordance Correlation Coefficient (CCC) of 0.333. Moreover, prompting the LLM with hybrid global-local patterns yields a relative CCC improvement of 9.6%. Qualitative interpretability analysis reveals trait-specific linguistic patterns, offering valuable insights for psychological research, computational linguistics, and paralinguistic studies. The proposed method thus advances both accuracy and transparency in PA, with promising applications in psychological profiling, personnel selection, and personalized recommendation systems.