构建大模型社会科学研究的系统性分类框架
The Emergence of Social Science of Large Language Models
- 通过文本嵌入与聚类分析，从270篇论文中提炼出三大研究领域
- 发现大模型在认知、道德和偏见方面引发人类心智投射现象
- 适合关注AI伦理、人机交互与群体智能的研究者参考
大语言模型的社会科学探讨这些系统如何引发认知归因、彼此互动，并改变人类活动与制度。我们对270项研究进行了系统综述，结合文本嵌入、无监督聚类与主题建模，构建了计算分类体系。三个领域自然涌现：大模型作为社会心智，研究模型行为是否以及何时引发认知、道德与偏见的归因，同时应对测试泄露与表面线索等挑战；大模型社会研究多智能体场景下，交互协议、架构与机制设计如何塑造协调、规范、制度与集体认知过程；大模型-人类交互研究模型如何重塑任务、学习、信任、工作与治理，以及人机界面的风险。该分类体系为碎片化领域提供可复现的路线图，明确各分析层次的证据标准，并指明人工智能社会科学的累积进展机会。
原文摘要 · Abstract (English)
The social science of large language models (LLMs) examines how these systems evoke mind attributions, interact with one another, and transform human activity and institutions. We conducted a systematic review of 270 studies, combining text embeddings, unsupervised clustering and topic modeling to build a computational taxonomy. Three domains emerge organically across the reviewed literature. LLM as Social Minds examines whether and when models display behaviors that elicit attributions of cognition, morality and bias, while addressing challenges such as test leakage and surface cues. LLM Societies examines multi-agent settings where interaction protocols, architectures and mechanism design shape coordination, norms, institutions and collective epistemic processes. LLM-Human Interactions examines how LLMs reshape tasks, learning, trust, work and governance, and how risks arise at the human-AI interface. This taxonomy provides a reproducible map of a fragmented field, clarifies evidentiary standards across levels of analysis, and highlights opportunities for cumulative progress in the social science of artificial intelligence.
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