LLM距离成为我们的数字孪生还有多远？一个基于角色的行为链模拟基准
How Far are LLMs from Being Our Digital Twins? A Benchmark for Persona-Based Behavior Chain Simulation
AI总结：
本文提出首个评估LLMs连续人类行为模拟能力的基准BehaviorChain，包含1001个角色的15846种行为，实验表明现有最先进模型仍难以准确模拟连续人类行为。
摘要：
近年来，大语言模型（LLMs）因其作为人类数字孪生的潜力而在各学科中受到越来越多的关注。数字孪生是旨在复制个体并自主代表其执行决策、问题解决和推理等任务的虚拟代理。然而，当前对LLMs的评估主要侧重于对话模拟，而忽视了对于数字孪生至关重要的人类行为模拟。为弥补这一不足，我们提出了BehaviorChain，这是首个用于评估LLMs模拟连续人类行为能力的基准。BehaviorChain包含多样化、高质量、基于角色的行为链，总计涵盖1,001个独特角色的15,846种不同行为，每个角色都具有详细的历史和画像元数据。在评估中，我们将角色元数据整合到LLMs中，并利用它们在BehaviorChain提供的动态场景中迭代推断上下文适当的行为。综合评估结果表明，即使是最先进的模型也难以准确模拟连续的人类行为。
英文摘要：
Recently, LLMs have garnered increasing attention across academic disciplines for their potential as human digital twins, virtual proxies designed to replicate individuals and autonomously perform tasks such as decision-making, problem-solving, and reasoning on their behalf. However, current evaluations of LLMs primarily emphasize dialogue simulation while overlooking human behavior simulation, which is crucial for digital twins. To address this gap, we introduce BehaviorChain, the first benchmark for evaluating LLMs' ability to simulate continuous human behavior. BehaviorChain comprises diverse, high-quality, persona-based behavior chains, totaling 15,846 distinct behaviors across 1,001 unique personas, each with detailed history and profile metadata. For evaluation, we integrate persona metadata into LLMs and employ them to iteratively infer contextually appropriate behaviors within dynamic scenarios provided by BehaviorChain. Comprehensive evaluation results demonstrated that even state-of-the-art models struggle with accurately simulating continuous human behavior.
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