3 ms·
This method is for training. They are using a stop-gradient to 'shield' some tokens from contributing to prediction of the immediate next token, and thus produc
by sdenton4 3y ago
This method is for training. They are using a stop-gradient to 'shield' some tokens from contributing to prediction of the immediate next token, and thus producing a stream of tokens that are only used for longer term prediction.
This is a bit more low level than the usual prompt engineering approaches, and to my mind, a bit more promising. There's more easily measurable results, and I've seen other context where a well placed stop-gradient does wonders...
- lucidrains 3y agoyes, it is a stop gradient mask on the attention matrix, iiuc. worth trying
- lucidrains 3y agocould even try it with a fraction of the attention heads, instead of introducing new tokens
- sdenton4 3y agoAn important piece here is that there's still a training signal making it to the makes weights. See SimSiam for a similar example.
- lucidrains 3y agoindeed, simsiam is a great example of the effectiveness of using stop gradient