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Ashish Vaswani is a machine learning researcher and coauthor of the 2017 paper introducing the Transformer model. The paper's shared-contribution statement credits him with jointly designing and implementing the first Transformer models. It explicitly states that author order is random, so his first position should not be read as sole credit for the architecture.^[ref]

The Transformer model uses self-attention to combine information from different positions in a sequence. In scaled dot-product attention, a query is compared with keys using dot products, then the softmax function supplies weights for combining the corresponding values. The model connects this mechanism to a complete neural network for tasks such as machine translation.^[ref]

An original one-dimensional example makes the weighting concrete. Choose query $q=1$, keys $k_1=0$, $k_2=\ln 3$, and values $v_1=2$, $v_2=6$. With key dimension $d_k=1$, the scaled scores are $0$ and $\ln 3$. The softmax function gives weights $1/4$ and $3/4$, so the output is $(1/4)2+(3/4)6=5$. The input values are mixed according to the query–key comparison; this invented example is not a learned translation result.

The original evaluation reported 28.4 BLEU for its large model on the English-to-German newstest2014 test. That is a result under a specific training and evaluation setup, rather than a universal score for all Transformer models.^[ref]

By Ashish Vaswani.

2017 paper; inspected arXiv version 7, 2 August 2023. Sections 5.4.2 and 6.2, Table 3: label smoothing and Transformer component variations on newstest2013. Base epsilon_ls=0.1: wordpiece perplexity 4.92 and BLEU 25.8; epsilon_ls=0.0: 4.67 and 25.3. Model-variation runs use no checkpoint averaging. Byline and contribution note identify Ashish Vaswani; other authors retain the published byline.