Understanding Transformers Part 6: Calculating Similarity Between Queries and Keys
embeddings
| Source: Dev.to | Original article
A new tutorial released on the Nordic AI hub deepens the series on transformer internals by showing exactly how similarity between queries and keys is computed in self‑attention. The post, “Understanding Transformers Part 6: Calculating Similarity Between Queries and Keys,” picks up where the April 12 article on queries, keys and similarity left off, and walks readers through the scaled dot‑product operation that underpins every modern large‑language model.
The author explains that each token’s query vector \(Q\) and every other token’s key vector \(K\) are first projected from the token embeddings by learned weight matrices. Their dot product yields a raw relevance score, which is then divided by \(\sqrt{d_k}\) – the square root of the key dimension – to temper the variance that grows with larger hidden sizes. A softmax across the resulting scores converts them into attention weights that sum to one, allowing the model to blend value vectors proportionally to their contextual relevance.
Why the focus matters is twofold. First, the similarity calculation determines which parts of a sequence influence each other, directly shaping the model’s ability to capture long‑range dependencies. Second, the scaling factor and softmax temperature have become levers for researchers tweaking stability and sparsity, influencing both training efficiency and inference speed on Nordic data‑center hardware. Misunderstanding this step can lead to sub‑optimal hyper‑parameter choices or unexpected bias in attention patterns.
Looking ahead, the series promises a seventh installment on the value matrix and multi‑head aggregation, followed by a deep dive into efficient attention approximations that are gaining traction in low‑latency applications. Readers interested in the practical implications for model compression and hardware acceleration should keep an eye on those releases, as they will likely shape the next wave of transformer‑based services across the region.
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