Large Language Models (LLMs) like GPT-4 encode words in a vast 12,288-dimensional space, evolving their meaning through contextual framing rather than fixed definitions. This process enables dynamic interpretations based on surrounding language. For instance, "apple" shifts from representing fruit to technology depending on context. Words in an LLM are not merely stored; they are strategically positioned, allowing the model to adapt their meanings in real time as each sentence unfolds. Thus, language becomes a fluid, geometrical representation rather than a series of static definitions.
The word "apple" doesn't mean anything by itself. It means everything in context—and that context is calculated.
In the world of LLMs, every word, token, or fragment of language isn't just stored-it's located or mapped.
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