Read the original Transformer paper
Attention Is All You Need introduced the Transformer in 2017 through machine-translation experiments.
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Attention Is All You Need introduced the Transformer architecture in 2017. Ashish Vaswani was one of eight authors, alongside Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. The work should be credited to that team. [1]
What the paper changed
The paper proposed a sequence-processing architecture based on attention, removing recurrence and convolutions from its core design. Its experiments focused on machine translation and reported improved quality with more parallelizable training. [1]
Start with the original problem
Read the architecture diagram alongside the translation task. Track how the input is represented, how attention connects positions and how the decoder produces output. Keep the paper's measured results separate from later applications of the architecture.
The social caption connects the work with modern assistants. The original paper itself is evidence for the architecture and its experiments, rather than a complete description of today's proprietary models.
Understanding that boundary makes the paper more useful: it gives you a concrete design to study without requiring claims about every system that followed.
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