Self-attention is often explained as “tokens look at each other.”
But internally, it is a precise two-step mechanism:
1️⃣ Relevance Scoring
For the current token, the model computes how important every other token in the sequence is.
It asks:
“Which words in this sentence matter for understanding me?”
This produces attention scores (via Query–Key similarity).
2️⃣ Combining Information
Once relevance is determined, the model performs a weighted combination of information from all tokens.
Important tokens contribute more.
Less relevant ones contribute less.
This produces a new representation that is:
✔ Context-aware
✔ Meaning-enriched
✔ Sensitive to long-range dependencies
🔬 Intuition
Before attention → a token only knows itself.
After attention → a token knows the entire sentence context.
Conceptually:
Relevance scoring = deciding who matters
Information combining = learning from them
This simple mechanism is what allows Transformers to model:
• Coreference resolution
• Long-distance relationships
• Contextual meaning shifts
• Complex linguistic structure
Understanding this deeply changes how you view LLMs — they are not memorizing sequences, they are dynamically re-weighting contextual information at every layer.
Currently exploring transformer internals, scaling behavior, and efficiency trade-offs in modern architectures.
Open to research discussions on attention mechanisms and efficient model design.
Image credit: DeepLearning.AI — “How Transformer LLMs Work” course.
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