Thanks a lot! I always felt weird about positional embeddings, because positions are not a set, they’re a continuum. My initial guess for why they don’t extrapolate was that the extrapolated embeddings step on the others’ turf once a few computations or layers are applied, causing the model to be confused about order, as if random concepts were inserted here and there. (Position overfit seems like it would weigh in though indeed.)

Have you experimented with nonlinear biases?

Is ALiBi still the sota for this setting, or have there been advances beyond this in the last 8 months? I know there has been a lot of interest in longer context lengths recently.

If I understand it correctly, you are only attending preceding tokens in your paper. Can the constant bias matrix be made symmetric for unmasked tasks?

I’m curious as to whether this inductive bias wouldn’t hurt on tasks where the first sentence of a long corpus would contain the most useful information.

Nonetheless, very clever trick and congrats on the great paper!