for text, you might realize that different parts of the text refer to each other, so need a way to effectively pass information around, and hence you end up with something like the attention mechanism
If you are trying to convince yourself that a Transformer could work and to make it ‘obvious’ to yourself that you can model sequences usefully that way, it might be a better starting point to begin with Bengio’s simple 2003 LM and MLP-Mixer. Then Transformers may just look like a fancier MLP which happens to implement a complicated way of doing token-mixing inspired by RNNs and heavily tweaked empirically to eke out a bit more performance with various add-ons and doodads.
(AFAIK, no one has written a “You Could Have Invented Transformers”, going from n-grams to Bengio’s LM to MLP-Mixer to RNN to Set Transformer to Vaswani Transformer to a contemporary Transformer, but I think it is doable and useful.)
If you are trying to convince yourself that a Transformer could work and to make it ‘obvious’ to yourself that you can model sequences usefully that way, it might be a better starting point to begin with Bengio’s simple 2003 LM and MLP-Mixer. Then Transformers may just look like a fancier MLP which happens to implement a complicated way of doing token-mixing inspired by RNNs and heavily tweaked empirically to eke out a bit more performance with various add-ons and doodads.
(AFAIK, no one has written a “You Could Have Invented Transformers”, going from n-grams to Bengio’s LM to MLP-Mixer to RNN to Set Transformer to Vaswani Transformer to a contemporary Transformer, but I think it is doable and useful.)