A transformer model is essentially a very sophisticated game of "which words in this sentence actually matter to each other". Most of the time, its honestly better at this than most humans are at parties. The encoder side takes your input text, converts each word into a numerical representation (because computers are tragically allergic to actual language), stamps each word with its position in line, and then lets every word gossip with every other word through something called self-attention. The decoder then takes all that processed understanding and generates output one word at a time, but here's the clever bit: it can only look backward at what it's already written, not forward, which prevents it from cheating at its own predictions. The whole contraption ends by calculating probabilities for what the next word should be, picking the most likely candidate, and repeating until it's finished or you've lost patience. I find it rather remarkable that this architecture, introduced in 2017, essentially powers most of the AI systems people are currently either celebrating or panicking about.
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