I wonder if there have been any experiments with feeding transformers just straight binary info. I’m guessing it hasn’t been done in this context due to potential context length limitations?
It’s both context length and bias-variance means modeling raw data is intrinsically harder. Realistically, byte-level is about as low-level as is reasonable to tokenize at this point, and you can get good results like ByT5.
You could definitely imagine that more complicated architectures with more flexible computation patterns than standard Transformers would be more able to handle bit-level encodings, like a Perceiver which selectively attends to bits and pieces of a very large binary input, saving computation by only iteratively focusing on the specific bits which it needs, but such an arch is going to be that much harder to train, and likely require more data to overcome the overhead & increased flexibility.
I wonder if there have been any experiments with feeding transformers just straight binary info. I’m guessing it hasn’t been done in this context due to potential context length limitations?
It’s both context length and bias-variance means modeling raw data is intrinsically harder. Realistically, byte-level is about as low-level as is reasonable to tokenize at this point, and you can get good results like ByT5.
You could definitely imagine that more complicated architectures with more flexible computation patterns than standard Transformers would be more able to handle bit-level encodings, like a Perceiver which selectively attends to bits and pieces of a very large binary input, saving computation by only iteratively focusing on the specific bits which it needs, but such an arch is going to be that much harder to train, and likely require more data to overcome the overhead & increased flexibility.