In the field of Artificial (General) Intelligence (AI), the several recent advancements in Natural language processing (NLP) activities relying on Large Language Models (LLMs) have come to encourage the adoption of LLMs as scientific models of language. While the terminology employed for the characterization of LLMs favors their embracing as such, it is not clear that they are in a place to offer insights into the target system they seek to represent. After identifying the most important theoretical and empirical risks brought about by the adoption of scientific models that lack transparency, we discuss LLMs relating them to every scientific model's fundamental components: the object, the medium, the meaning and the user. We conclude that, at their current stage of development, LLMs hardly offer any explanations for language, and then we provide an outlook for more informative future research directions on this topic.

翻译：在人工（通用）智能领域，近年来依赖大型语言模型（LLMs）的自然语言处理（NLP）活动取得多项进展，促使LLMs被采用为语言的科学模型。尽管用于描述LLMs的术语支持其作为此类模型的应用，但它们能否为所代表的目标系统提供见解尚不明确。在识别缺乏透明性的科学模型所带来的最重要理论和实证风险后，我们结合每个科学模型的基本组成要素——对象、媒介、意义和使用者——对LLMs进行讨论。结论表明，在目前的发展阶段，LLMs几乎无法对语言提供任何解释，并在此基础上展望了更具信息价值的未来研究方向。