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：人类倾向于在周围物体中看到类似“人类”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，在机器学习领域也逐渐兴起——大型语言模型被宣称展现出类人智能。在本立场论文中，我们结合职业激励、人类偏见及通用方法论设置，探讨当前对人工通用智能的追求如何成为过度赋予大型语言模型类人特质的完美风暴。通过多项实验，我们证明潜空间中人类可解释模式的发现本不应令人惊讶。同时考虑媒体常见的人工智能描述方式，我们呼吁学术界在解读与传播人工智能研究成果时，需格外谨慎，并时刻遵循学术诚信原则。