让大模型学会根据性格特质理解情绪，提升情感推理能力。
PTEI: Integrating Personality Traits to Enhance Emotional Intelligence in Large Language Models
- 用MBTI和OCEAN人格特质构建个性化提示，引导模型推断情绪。
- 结合对比学习检索相关情境，使推理更贴合人性。
- 在多个基准上显著提升大模型情感理解力，尤其适合对话系统应用。
尽管情感智能取得进展，大型语言模型在复杂情感推理方面仍远低于人类表现。这一差距部分源于个体差异（特别是人格特质）的缺失，而人格特质是人类情感推断的基础。为此，我们提出PTEI框架，通过将人格特质融入情感智能任务中的大模型。PTEI直接从情感场景中提取MBTI与OCEAN人格特质，并将其作为上下文知识嵌入人格感知提示中，引导模型准确推断情绪及其深层原因。为确保上下文精准对齐，采用对比学习构建优化检索系统，提升情境匹配度与推理质量。在多个主流情感智能基准上的实验证明，PTEI显著增强各类大模型的情感理解能力，其中以GPT系列提升最明显；结合思维链（CoT）推理可额外提升4%准确率。结果表明，PTEI有助于推动具备更深层次社会心理基础的AI系统发展。
原文摘要 · Abstract (English)
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.
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