发表机构
Telecom SudParis, Institut Polytechnique de Paris; Aberystwyth University（巴黎电信学院，巴黎综合理工学院; 阿伯里斯特威斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对大语言模型在复杂情感推理中不如人类的问题，提出PTEI框架，通过提取人格特质并用于人格感知提示引导模型推理，结合对比学习优化检索系统，经实验验证其能增强模型情感理解能力，与CoT推理结合可进一步提升准确率。
AI 中文摘要
尽管情商（EI）取得了进展，但大语言模型（LLMs）在复杂情感推理方面仍明显不如人类。这种差距部分源于对个体差异（特别是人格特质，它是人类情感推理的基础）的整合有限。为解决此问题，我们提出PTEI，这是一个使用LLMs将人格特质整合到情商任务中的新框架。在PTEI中，首先从给定情感场景中直接提取MBTI和OCEAN人格特质，然后将其用作人格感知提示中的上下文知识，引导LLMs准确推断情感及其潜在原因。为确保最佳上下文基础，我们采用对比学习构建优化检索系统，呈现情感和个人匹配的场景，提高推理质量。在既定EI基准上的大量实验表明，PTEI增强了各种LLMs的情感理解（EU）能力，在GPT模型中提升最强。将PTEI与思维链（CoT）推理相结合，准确率额外提高4%。这些发现强调了PTEI对推进具有更复杂社会和心理基础的人工智能系统的贡献。
英文摘要
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 Emotional Intelligence 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. Combining PTEI with Chain-of-Thought (CoT) reasoning yields an additional 4 percent increase in accuracy. These findings underscore PTEI's contribution toward advancing AI systems with more sophisticated social and psychological grounding.
