PTEI: Integrating Personality Traits to Enhance Emotional Intelligence in Large Language Models

ACL ARR 2025 May Submission3488 Authors

Abstract: Despite advances in Emotional Intelligence (EI), Large Language Models (LLMs) still significantly underperform humans in complex emotional reasoning. This gap originates partly from the limited incorporation of individual differences, particularly personality traits, which are fundamental to human emotional inference. To address this, we propose PTEI, a novel framework for integrating personality traits into EI tasks using LLMs. In PTEI, MBTI and OCEAN personality traits are first extracted directly from the given emotional scenarios and then utilized as contextual knowledge within personality-aware prompts, guiding LLMs to accurately infer emotions and their underlying causes. To ensure optimal contextual grounding, we employ contrastive learning to construct an optimized retrieval system that surfaces emotionally and personally aligned scenarios, enhancing reasoning quality. Extensive experiments on established EI benchmarks show that PTEI enhances the emotional understanding (EU) capabilities of various LLMs, with the strongest improvement observed in GPT models, where combining PTEI with Chain-of-Thought (CoT) reasoning yields an additional 4% increase in accuracy. These findings underscore PTEI's contribution toward advancing AI systems with more sophisticated social and psychological grounding.

Paper Type: Long

Research Area: Computational Social Science and Cultural Analytics

Research Area Keywords: emotion detection and analysis, human behavior analysis, Language Modeling

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Data analysis

Languages Studied: English

Submission Number: 3488