Right now, BigBird is encoder only so it doesn't generate text. Causal attention with the global memory is a bit weird, but we could probably do it.

GPT-3 is only using a sequence length of 2048. In most of our paper, we use 4096, but we can go much larger 16k+. Of course, we don't nearly have as many parameters as GPT-3, so our generalization may not be as good.

BigBird is just an attention mechanism and could actually be complementary to GPT-3.

I think the insight is not incredibly original and follows naturally from OpenAI's Sparse Transformer. The idea is similar to Longformer. Two teams at Google had a similar insight, hence the high number of authors.

The original implementation only took a couple of months and was primarily motivated by internal Google applications. Natural Questions was the first external benchmark we tried to validate on, which took a few months to find the right setup. All the other datasets, took a few weeks but the effort was done in parallel given the large team.

There was quite a bit of frustration dealing with Tensorflow, TPUs, and the XLA compiler that maybe set us back a few months, too.

This is something we want to explore. BigBird just replaces the attention mechanism in BERT.

We believe something like BigBird can be complementary to GPT-3. GPT-3 is still limited to 2048 tokens. We'd like to think that we could generate longer, more coherent stories by using more context.

Not really. The proofs are more of a curiosity really. I think the strong performance on QA takes that require multihop reasoning give some evidence that the model is capable of complex reasoning.