Agentic Society: Merging skeleton from real world and texture from Large Language Model
<|reference_start|>Agentic Society: Merging skeleton from real world and texture from Large Language Model: Recent advancements in large language models (LLMs) and agent technologies offer promising solutions to the simulation of social science experiments, but the availability of data of real-world population required by many of them still poses as a major challenge. This paper explores a novel framework that leverages census data and LLMs to generate virtual populations, significantly reducing resource requirements and bypassing privacy compliance issues associated with real-world data, while keeping a statistical truthfulness. Drawing on real-world census data, our approach first generates a persona that reflects demographic characteristics of the population. We then employ LLMs to enrich these personas with intricate details, using techniques akin to those in image generative models but applied to textual data. Additionally, we propose a framework for the evaluation of the feasibility of our method with respect to capability of LLMs based on personality trait tests, specifically the Big Five model, which also enhances the depth and realism of the generated personas. Through preliminary experiments and analysis, we demonstrate that our method produces personas with variability essential for simulating diverse human behaviors in social science experiments. But the evaluation result shows that only weak sign of statistical truthfulness can be produced due to limited capability of current LLMs. Insights from our study also highlight the tension within LLMs between aligning with human values and reflecting real-world complexities. Thorough and rigorous test call for further research. Our codes are released at https://github.com/baiyuqi/agentic-society.git<|reference_end|>
@article{bai2024agentic,
    title={Agentic Society: Merging skeleton from real world and texture from Large
  Language Model},
    author={Yuqi Bai, Kun Sun, Huishi Yin},
    journal={arXiv preprint arXiv:2409.10550},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10550},
    primaryClass={cs.CY cs.CL}
}