Words are just points on many number lines that capture part of the meaning.
Self-attention in large language models (LLMs) finally made sense when I visualized words as points in 12,000 dimensions—this mental model changed everything for me.
Here’s what you’ll learn:
How LLMs represent words in high-dimensional space to capture nuanced meanings.
How self-attention updates word meanings dynamically based on context.
Why this understanding is key to grasping how AI understands language.
By the end of this video, you’ll have a clear picture of how words, context, and attention interact to make LLMs so powerful.
If you’ve struggled to understand self-attention, I hope this visual change can help make it “click” for you.