How Far are LLMs from Being Our Digital Twins? A Benchmark for Persona-Based Behavior Chain Simulation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915399584448512 |
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| author | Li, Rui Xia, Heming Yuan, Xinfeng Dong, Qingxiu Sha, Lei Li, Wenjie Sui, Zhifang |
| author_facet | Li, Rui Xia, Heming Yuan, Xinfeng Dong, Qingxiu Sha, Lei Li, Wenjie Sui, Zhifang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_14642 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | How Far are LLMs from Being Our Digital Twins? A Benchmark for Persona-Based Behavior Chain Simulation Li, Rui Xia, Heming Yuan, Xinfeng Dong, Qingxiu Sha, Lei Li, Wenjie Sui, Zhifang Computation and Language 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. |
| title | How Far are LLMs from Being Our Digital Twins? A Benchmark for Persona-Based Behavior Chain Simulation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2502.14642 |