Enhancing Abstraction in Large Language Models Through Nature-Inspired Semantic Patterns

Recent advancements in artificial intelligence emphasize improving the abstraction capabilities of Large Language Models (LLMs) by integrating structured knowledge and sophisticated neural architectures. This paper proposes a novel approach grounded in a fourfold pattern observed at multiple levels of granularity in nature, referred to as ‘ $\mathrm{Kn}^{\prime}$, ‘Po’, ‘Pr’, and ‘H’, which correlates with concepts of knowledge, presence, power, and harmony. We hypothesize that leveraging this intrinsic structure in language could enhance LLMs’ ability to abstract underlying themes, recognize subtle relationships, and infer unstated implications, similar to the hierarchical complexity seen in natural systems from quantum particles to cellular structures. By incorporating these elements into the transformer architecture of LLMs, by enhancing self-attention through additional $\mathrm{Q}, \mathrm{K}, \mathrm{V}$ weights to thereby also optimize computational requirements, we aim to demonstrate a more nuanced understanding and generation of text, at least paralleling and perhaps even moving beyond normal humanlike comprehension and reasoning.