Exercise 2: The Different Jobs of the Attention and Feed-Forward Sublayers — Possible Solution ==================================================================== WHAT THE MULTI-HEAD ATTENTION SUBLAYER'S JOB ACTUALLY IS ------------------------------ Per this chapter, "attention's own job is mixing information across tokens — deciding what each token should attend to." Every token's own output from this sublayer is a weighted blend of information drawn from other tokens in the sequence (via the Value vectors, weighted by attention scores computed from Query/Key comparisons per llm1-4). The defining feature of this sublayer is that it moves information between different positions in the sequence. WHAT THE FEED-FORWARD SUBLAYER'S JOB ACTUALLY IS INSTEAD ------------------------------ Per this chapter, the feed-forward network is "applied identically and separately to each position" — every token's own vector is transformed by the same small two-layer network, but completely independently of every other token's vector. No information is exchanged between positions here at all; each token is processed in isolation, using whatever information attention already gathered for it in the previous sublayer. WHY THIS IS A GENUINE DIVISION OF LABOR, NOT REDUNDANCY ------------------------------ Per this chapter, "attention decides what to look at; the feed-forward layer decides what to do with it, position by position." These are sequential, complementary steps: attention first gathers relevant information from across the sequence into each token's own vector, and only then does the feed-forward layer apply additional non-linear transformation to what's now sitting in that vector. Removing attention would leave the feed-forward layer processing each token in total isolation, with no way to ever incorporate information from other tokens at all. Removing the feed-forward layer would leave attention's own linear combination of Values as the only transformation available, with no additional non-linear processing capacity applied to what attention gathered. WHY BOTH ARE NEEDED, NOT JUST ONE ------------------------------ Attention alone can mix information across positions, but scaled dot-product attention (llm1-4) is fundamentally a weighted linear combination of Value vectors — it doesn't, by itself, apply the kind of non-linear transformation that gives a network its ability to represent complex functions. The feed-forward sublayer alone could apply non-linear transformation, but with no attention step before it, each token would only ever have access to its own original information, never anything drawn from elsewhere in the sequence. Only together — attention gathering cross-token context, then feed-forward transforming what was gathered — does a Transformer block get both capabilities at once. WHY THIS WORKS AS AN ANSWER ------------------------------ It defines each sublayer's specific job using this chapter's own language, explains why the two are sequential and complementary rather than interchangeable, and shows concretely what capability would be lost if either sublayer were removed from the block.