EMMR: Emotion-Mediated Multimodal Reasoning for Personality Assessment in Asynchronous Video Interviews

Abstract

Asynchronous Video Interviews (AVIs) have become increasingly popular for personality assessment. Recent large language models (LLMs) have shown potential for personality assessment from transcribed interview responses. However, text-centered methods may overlook non-verbal behavioral cues conveyed through visual and audio modalities, even though such cues are highly relevant to personality assessment. In particular, emotion-related cues provide important social and affective evidence for understanding candidates' behavior related to personality traits. Thus, we propose EMMR (Emotion-Mediated Multimodal Reasoning), a two-stage framework for MLLMs-based personality assessment for AVIs. EMMR extracts emotion-related cues from multimodal interview data and incorporates them into personality assessment through structured reasoning as auxiliary social and behavioral evidence. Experiments on two AVIs datasets, OPVA and AVI-6, show that EMMR improves MAE, MSE, and PCC compared with baselines. Further analysis indicates that semantic descriptions of emotion cues enhance personality assessment, while their quality affects personality assessment reliability. These results suggest that integrating emotion-related cues into multimodal reasoning is a promising direction for more interpretable MLLMs-based personality assessment in AVIs.