用人格特质提升大模型情感支持对话的个性化与共情力
From Personas to Talks: Revisiting the Impact of Personas on LLM-Synthesized Emotional Support Conversations
- 基于心理学框架为大模型注入人格特质，生成情感支持对话
- 人格特质微调影响对话情绪和外向性，改变支持策略分布
- 适合构建更贴心、更个性化的AI心理咨询系统
大语言模型（LLMs）的快速发展推动了情感支持对话（ESC）的生成，实现低成本、高可扩展性与更强的数据隐私保护。本文研究了人格特质在LLM生成情感支持对话中的作用。通过采用成熟的心理学框架，我们测量并注入人格特征至模型，使其生成情境化对话。大规模评估揭示：1）LLMs能有效识别核心人格特质；2）情绪性和外向性出现细微变化，影响对话动态；3）人格特征的引入改变了情感支持策略的分布，提升了回应的相关性与共情质量。这些发现表明，人格驱动的LLM在构建更具个性化、共情力和效率的情感支持对话方面具有巨大潜力，对未来AI情感支持系统的设计具有重要意义。
原文摘要 · Abstract (English)
The rapid advancement of Large Language Models (LLMs) has revolutionized the generation of emotional support conversations (ESC), offering scalable solutions with reduced costs and enhanced data privacy. This paper explores the role of personas in the creation of ESC by LLMs. Our research utilizes established psychological frameworks to measure and infuse persona traits into LLMs, which then generate dialogues in the emotional support scenario. We conduct extensive evaluations to understand the stability of persona traits in dialogues, examining shifts in traits post-generation and their impact on dialogue quality and strategy distribution. Experimental results reveal several notable findings: 1) LLMs can infer core persona traits, 2) subtle shifts in emotionality and extraversion occur, influencing the dialogue dynamics, and 3) the application of persona traits modifies the distribution of emotional support strategies, enhancing the relevance and empathetic quality of the responses. These findings highlight the potential of persona-driven LLMs in crafting more personalized, empathetic, and effective emotional support dialogues, which has significant implications for the future design of AI-driven emotional support systems.
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