Comment by pointlessone
2 months ago
I’m still confused. Does it treat the input tokens as a sampled waveform?
I mean, say I have some text file in ASCII. Do I then just pretend it’s raw wav and do FFT on it? I guess it can give me some useful information (like does it look like any particular natural language or is it just random; sometimes used in encrytion analysis of simple substitution cyphers). It feels surprising that revers FFT can get a coherent output after fiddling with the distribution.
Do keep in mind that FFT is a lossless, equivalent representation of the original data.
As I understand it, the token embedding stream would be equivalent to multi-channel sampled waveforms. The model either needs to learn the embeddings by back-propagating through FFT and IFFT, or use some suitable tokenization scheme which the paper doesn't discuss (?).
It seems unlikely to work for language.
It embeds them first into vectors. The input is a real matrix with (context length)x(embedding size) dimensions.
No. The FFT is an operation on a discrete domain, it is not the FT. In the same way audio waveforms are processed by an FFT you bucket frequencies which is conceptually a vector. Once you have a vector, you do machine learning like you would with any vector (except you do some FT in this case, I haven’t read the paper).
Most likely the embedding of the token is passed through FFT