Vectoring Languages

TL;DR

This paper proposes a novel language structure inspired by linear algebra that aligns with language model mechanisms and better captures language diversity, offering new research directions for accelerating scientific progress.

Contribution

It introduces a new language structure reflecting language model mechanisms, bridging linguistic theory and AI, and suggests pathways for future research to enhance scientific advancements.

Findings

The proposed structure aligns with language model mechanisms.

It better captures the diverse nature of language.

Discussion on research directions for scientific acceleration.

Abstract

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.