这篇论文用《帝国时代 II》戳破了 LLM 拟人属性的独特性神话，做 AI 伦理或认知科学的研究者值得一看——它提醒我们，不要轻易给模型贴人性标签，否则结论可能站不住脚。
研究者通过训练一个简单神经网络玩《帝国时代 II》，指出大型语言模型（LLM）的拟人属性（如道德、自然语言理解）并非其独有。任何足够强大的基板（如乐高或大波士顿地区）都可能表现出类似特征，因此这些属性在经验上不唯一。论文强调，讨论 LLM 的拟人属性需要明确的测量标准，否则结论可能循环或空洞。作者提出“非唯一性”作为零假设，建议实验设计时先假设 LLM 不具独特性，并证明《帝国时代 II》是图灵完备的。该工作挑战了当前 AI 拟人化研究的假设基础。
If LLMs Have Human-Like Attributes, Then So Does Age of Empires II
Much research has been carried out on large language models (LLMs) and LLM-powered agentic workflows. However, many works within the field state emergence of, ascribe to, or assume, generalised anthropomorphic attributes to them (e.g., morality or understanding of natural language). Our goal is not to argue in favour or against the existence of these attributes, but to point out that these conclusions could be incorrect. For this we build and train a simple neural network on the videogame Age of Empires II, and note that any entity in a sufficiently-powerful substrate, such as LEGO or the Greater Boston Area, could also present such attributes. Hence, the purported anthropomorphic attributes of LLMs are empirically non-unique: although some properties (e.g., responses to prompts) could remain constant, others, such as the interpretation of their perceived behaviour, might change with the substrate. Thus, any empirically-grounded discussion requires explicit measurement criteria; otherwise the interpretation is left to the representation. We then show that assuming that these attributes exist or not in a system, independent of the substrate and in a generalised way, leads to either circular or uninformative conclusions, regardless of the experimenter's viewpoint on the subject. Finally we propose a 'null' assumption, where one assumes LLM non-uniqueness instead of assuming anthropomorphic attributes to set up an experiment, along with examples of it. We also discuss potential objections to our work, briefly survey the field, and prove that \textit{Age of Empires II} is functionally- and Turing-complete.