TwinVoice：一个通过大语言模型角色模拟实现数字孪生的多维度基准
TwinVoice: A Multi-dimensional Benchmark Towards Digital Twins via LLM Persona Simulation
AI总结：
针对当前大语言模型角色模拟评估的局限，该研究推出多维度基准TwinVoice，从三类场景、六项核心能力展开评估，发现现有模型表现仍远低于人类基线。
摘要：
大语言模型（LLM）正展现出类人的涌现能力，且越来越被视为模拟个体沟通风格、行为倾向与人格特质的基础。然而，当前基于LLM的角色模拟评估仍存在局限：多数依赖合成对话，缺乏系统性框架，也未对能力要求展开分析。为弥补这些不足，我们推出TwinVoice，这是一个用于在多样真实场景下评估角色模拟效果的综合性基准。TwinVoice涵盖三个维度：社会角色（公开社交互动）、人际角色（私人对话）和叙事角色（基于角色的表达）。它进一步将LLM性能评估拆解为六项核心能力，包括观点一致性、记忆召回、逻辑推理、词汇保真度、角色语气和句法风格。实验结果显示，尽管先进模型在角色模拟中达到了中等准确率，但在句法风格、记忆召回等能力上仍有不足。因此，LLM的平均表现仍远低于人类基线水平。
英文摘要：
Large Language Models (LLMs) are exhibiting emergent human-like abilities and are increasingly envisioned as the foundation for simulating an individual's communication style, behavioral tendencies, and personality traits. However, current evaluations of LLM-based persona simulation remain limited: most rely on synthetic dialogues, lack systematic frameworks, and lack analysis of the capability requirement. To address these limitations, we introduce TwinVoice, a comprehensive benchmark for assessing persona simulation across diverse real-world contexts. TwinVoice encompasses three dimensions: Social Persona (public social interactions), Interpersonal Persona (private dialogues), and Narrative Persona (role-based expression). It further decomposes the evaluation of LLM performance into six fundamental capabilities, including opinion consistency, memory recall, logical reasoning, lexical fidelity, persona tone, and syntactic style. Experimental results reveal that while advanced models achieve moderate accuracy in persona simulation, they still fall short of capabilities such as syntactic style and memory recall. Consequently, the average performance achieved by LLMs remains considerably below the human baseline.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
