When large language models (LLMs) process text, they convert words into numerical vectors that live in what’s called a high-dimensional space. Imagine each idea, word, or phrase as a point floating in a room, but instead of three dimensions, the model might have hundreds or even thousands of dimensions. These many dimensions allow the model to capture subtle nuances: meanings, contexts, and relationships all at once. It’s a bit like having a massive web of clues that tie together to form an overall picture of the word’s “personality.”
Our brains do something comparable when making sense of the world. We don’t think in simple, linear patterns—rather, we draw on networks of neurons, each responsible for different pieces of information. Concepts overlap and combine, much like overlapping areas in the model’s high-dimensional space. This layered approach is why both humans and language models can find connections between ideas that might not seem obvious at first glance.
When you see how these points group, you might notice words or ideas that share common traits naturally clustering together—just as similar memories or associations often link together in the human mind. The beauty of this approach is that it allows for a flexible, more intuitive way to store and recall information, helping models (and people) connect dots across vast seas of data.