The widespread success of large language models (LLMs) has been met with skepticism that they possess anything like human concepts or meanings. Contrary to claims that LLMs possess no meaning whatsoever, we argue that they likely capture important aspects of meaning, and moreover work in a way that approximates a compelling account of human cognition in which meaning arises from conceptual role. Because conceptual role is defined by the relationships between internal representational states, meaning cannot be determined from a model's architecture, training data, or objective function, but only by examination of how its internal states relate to each other. This approach may clarify why and how LLMs are so successful and suggest how they can be made more human-like.

翻译：大型语言模型(LLMs)的广泛成功受到怀疑,怀疑它们拥有任何与人类概念或含义相似的东西。 与LLMs没有任何意义的说法相反,我们争辩说,这些模型可能抓住了意义的重要方面,此外,它们的工作方式也接近了人类认知的令人信服的说明,其含义来自概念作用。 由于概念作用是由内部代表国家之间的关系所定义的,因此意义无法从模型的结构、培训数据或客观功能中确定,而只能通过研究其内部国家之间的关系来决定。 这种方法可以澄清LLMs为何和如何如此成功,并且建议如何使其更像人。