用游戏《帝国时代2》证明大模型的'类人属性'并非独有，而是依赖于系统本身。
If LLMs Have Human-Like Attributes, Then So Does Age of Empires II
- 在《帝国时代2》中训练简单神经网络，模拟出类似人类的属性表现。
- 任何强计算系统（如乐高或城市）都可能表现出类似行为，属性不具唯一性。
- 主张大模型具有类人属性是循环论证，应以‘非唯一性’为默认假设。
大量研究关注大语言模型（LLMs）及其代理工作流，但许多研究声称、归因或假设其具备泛化的人类属性（如道德或自然语言理解）。本文不争论这些属性是否存在，而是指出此类结论可能错误。为此，我们在游戏《帝国时代2》中构建并训练了一个简单的神经网络，发现任何足够强大的系统（如乐高积木或波士顿大区）也可能表现出类似属性。因此，大模型所呈现的所谓类人属性在经验上并不独特：尽管某些行为（如对提示的响应）可能保持不变，但对其行为的解读却随系统基底变化。任何基于实证的讨论必须明确测量标准，否则解释仅取决于表征方式。我们进一步表明，无论实验者立场如何，也不论结果是否显示属性存在，孤立地假设系统具有普遍类人属性都会导致循环或无信息结论。最后，我们提出‘零假设’——即默认大模型不具备唯一性，以此作为实验起点，并提供实例。同时讨论了潜在反对意见，简要综述领域现状，并证明《帝国时代2》在功能和图灵完备性上均成立。
原文摘要 · Abstract (English)
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 invariant, others, such as the interpretation of their perceived behaviour, might change with the substrate. Thus, any empirically-grounded discussion on these attributes 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. This is regardless of the experimenter's viewpoint on the subject, or whether the outcome shows existence or non-existence. 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 Age of Empires II is functionally- and Turing-complete.
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