Individual Turing Test: A Case Study of LLM-based Simulation Using Longitudinal Personal Data

Large Language Models (LLMs) have demonstrated remarkable human-like capabilities, yet their ability to replicate a specific individual remains underexplored. This paper presents a case study investigating LLM-based individual simulation using a volunteer-contributed archive of private messaging history spanning over ten years. Based on this dataset, we propose the ''Individual Turing Test'' to evaluate whether acquaintances of the volunteer can correctly identify which response in a multi-candidate pool most plausibly originates from the volunteer. We investigate prevalent approaches to LLM-based individual simulation, including fine-tuning, retrieval-augmented generation (RAG), memory-based methods, and hybrid approaches that integrate fine-tuning with RAG or memory. Empirical results show that current methods do not pass the Individual Turing Test, but perform substantially better when the same test is conducted on strangers to the target individual. Additionally, while fine-tuning improves performance in daily chats that reflect the individual's language style, retrieval-augmented and memory-based approaches demonstrate stronger performance on questions involving personal opinions and preferences. These findings reveal a fundamental trade-off between parametric and non-parametric approaches to individual simulation with LLMs under longitudinal context.