# Enhancing Personality Recognition by Comparing the Predictive Power of Traits, Facets, and Nuances

arXiv: https://arxiv.org/abs/2602.05650 · HTML: https://mlsanity.com/paper/2602.05650/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 5 Feb 2026.

## Builds on (most cited first, 4 of 4)

- [Multimodal Transformer for Unaligned Multimodal Language Sequences](https://mlsanity.com/paper/1906.00295/) (2019)
- [Context-Aware Personality Inference in Dyadic Scenarios: Introducing the UDIVA Dataset](https://mlsanity.com/paper/2012.14259/) (2020)
- [An Open-source Benchmark of Deep Learning Models for Audio-visual Apparent and Self-reported Personality Recognition](https://mlsanity.com/paper/2210.09138/) (2022)
- [Twenty Years of Personality Computing: Threats, Challenges and Future Directions](https://mlsanity.com/paper/2503.02082/) (2025)

## Abstract (arXiv)

Personality is a complex, hierarchical construct typically assessed through item-level questionnaires aggregated into broad trait scores. Personality recognition models aim to infer personality traits from different sources of behavioral data. However, reliance on broad trait scores as ground truth, combined with limited training data, poses challenges for generalization, as similar trait scores can manifest through diverse, context dependent behaviors. In this work, we explore the predictive impact of the more granular hierarchical levels of the Big-Five Personality Model, facets and nuances, to enhance personality recognition from audiovisual interaction data. Using the UDIVA v0.5 dataset, we trained a transformer-based model including cross-modal (audiovisual) and cross-subject (dyad-aware) attention mechanisms. Results show that nuance-level models consistently outperform facet and trait-level models, reducing mean squared error by up to 74% across interaction scenarios.