Humans have a tendency to see 'human'-like qualities in objects around them. We name our cars, and talk to pets and even household appliances, as if they could understand us as other humans do. This behavior, called anthropomorphism, is also seeing traction in Machine Learning (ML), where human-like intelligence is claimed to be perceived in Large Language Models (LLMs). In this position paper, considering professional incentives, human biases, and general methodological setups, we discuss how the current search for Artificial General Intelligence (AGI) is a perfect storm for over-attributing human-like qualities to LLMs. In several experiments, we demonstrate that the discovery of human-interpretable patterns in latent spaces should not be a surprising outcome. Also in consideration of common AI portrayal in the media, we call for the academic community to exercise extra caution, and to be extra aware of principles of academic integrity, in interpreting and communicating about AI research outcomes.

翻译：人类倾向于在所接触的物体中看到类似“人”的特质。我们为自己的汽车取名，与宠物甚至家用电器交谈，仿佛它们能像其他人一样理解我们。这种被称为“拟人化”的行为，在机器学习（ML）领域中也正得到关注，大型语言模型（LLMs）据称被观察到具有类似人类的智能。在这篇立场论文中，结合职业激励、人类偏见及一般性方法论设置，我们探讨了当前对通用人工智能（AGI）的探索如何在完美风暴中过度赋予LLMs人类特质。通过多项实验，我们证明在潜在空间中发现可解释的人类模式并非意外结果。同时，考虑到媒体对人工智能的常见描述，我们呼吁学术界在解读和传播人工智能研究成果时，格外谨慎并充分恪守学术诚信原则。