Recent breakthroughs in large language models (LLM) have stirred up global attention, and the research has been accelerating non-stop since then. Philosophers and psychologists have also been researching the structure of language for decades, but they are having a hard time finding a theory that directly benefits from the breakthroughs of LLMs. In this article, we propose a novel structure of language that reflects well on the mechanisms behind language models and go on to show that this structure is also better at capturing the diverse nature of language compared to previous methods. An analogy of linear algebra is adapted to strengthen the basis of this perspective. We further argue about the difference between this perspective and the design philosophy for current language models. Lastly, we discuss how this perspective can lead us to research directions that may accelerate the improvements of science fastest.

翻译：大型语言模型（LLM）的最新突破已引发全球关注，相关研究自此持续加速推进。哲学家与心理学家虽已研究语言结构数十年，却难以建立能直接受益于LLM突破的理论体系。本文提出一种能充分反映语言模型底层机制的新型语言结构，并论证该结构相较于既有方法更能捕捉语言的本质多样性。我们引入线性代数类比以强化该理论框架的基础，进而探讨该视角与当前语言模型设计理念的差异。最后，我们论述这一视角如何引领可能加速科学进步的研究方向。