“let the embedding be a latent space that forms naturally when training the network” The embeddings are produced in concert with the network, to serve the netw

“let the embedding be a latent space that forms naturally when training the network” The embeddings are produced in concert with the network, to serve the network, and not created as a separate step. It’s actually very cool The look-up table is a matrix. Each row is an embedding and each row number is a token ID. You get a differentiable transformation from token ID to token embedding using a “one hot vector” and a matrix multiplication If you take the transpose of this matrix, you can convert an internal representation back to the same token form, but treat it as logits and give it to the sampler. So token embeddings are produced on demand in service of the model, according to the model’s needs. I found an example of this strategy in a paper as far back as 1980! In the other reply I recommend the Bengio paper. But do bite the bullet and try it.