冲突对话中的行为对齐评估：LLM智能体与人类的多维比较
Evaluating Behavioral Alignment in Conflict Dialogue: A Multi-Dimensional Comparison of LLM Agents and Humans
AI总结：
本研究通过模拟含谈判的冲突对话，从语言风格、情感表达和策略行为三个维度比较人格提示LLM与人类的行为对齐，发现GPT-4.1和Claude-3.7-Sonnet各有所长但仍存在显著差距，为社交复杂交互中的LLM对齐建立了基准。
摘要：
大语言模型（LLMs）越来越多地被部署于社会复杂、交互驱动的任务中，但它们在情感和策略复杂情境下镜像人类行为的能力仍未得到充分探索。本研究通过在对抗性争议解决中模拟包含谈判的多轮冲突对话，评估了带有人格提示的LLMs的行为对齐程度。每个LLM都由匹配的五因素人格画像引导，以控制个体差异并增强真实感。我们从三个维度评估对齐：语言风格、情感表达（例如愤怒动态）和策略行为。GPT-4.1在语言风格和情感动态上与人类的对齐程度最接近，而Claude-3.7-Sonnet最能反映策略行为。尽管如此，仍存在显著的对齐差距。我们的研究结果为LLMs与人类在社会复杂交互中的对齐建立了基准，同时凸显了人格条件化在对话建模中的前景与局限。
英文摘要：
Large Language Models (LLMs) are increasingly deployed in socially complex, interaction-driven tasks, yet their ability to mirror human behavior in emotionally and strategically complex contexts remains underexplored. This study assesses the behavioral alignment of personality-prompted LLMs in adversarial dispute resolution by simulating multi-turn conflict dialogues that incorporate negotiation. Each LLM is guided by a matched Five-Factor personality profile to control for individual variation and enhance realism. We evaluate alignment across three dimensions: linguistic style, emotional expression (e.g., anger dynamics), and strategic behavior. GPT-4.1 achieves the closest alignment with humans in linguistic style and emotional dynamics, while Claude-3.7-Sonnet best reflects strategic behavior. Nonetheless, substantial alignment gaps persist. Our findings establish a benchmark for alignment between LLMs and humans in socially complex interactions, underscoring both the promise and the limitations of personality conditioning in dialogue modeling.
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