The article explores the parallel between large language models (LLMs) and theoretical physics, particularly string theory, positing that LLMs navigate a 12,288-dimensional space to generate language. Each word is represented as a point in this space, embodying various semantic dimensions. The act of predicting the next word is described through the mathematical process of measuring vector alignment via dot products. This insight highlights how complex geometric relationships underlie language generation, suggesting deeper connections between AI and the fundamental nature of reality.
In large language models (LLMs), every word you see is represented as a point in a multidimensional space, encoding aspects of meaning and context.
The high-dimensional space that LLMs operate within may share similarities with hidden dimensions in string theory, as both involve complex geometric structures.
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