tf-idf and similar heuristics are what we were using before attention came along, e.g. tf-idf weighted bag-of-words representation of word2vec embeddings. That approaches fails in so many cases.
If you don’t have attention and need a fast substitute for “forgetting” non important tokens, then BM25 is an intuitive hypothesis.
Attention makes it so that the weights of each token can be different in each sequence of tokens. Same token gets different weights depending on who its ‘neighbors’ in the sequence end up being.
This property allows the models to solve a variety of natural language problems and gets ‘used’ by the model to express context-aware dependencies.
My response was trying to clarify some confusion.
I am all for alternatives to attention. I don’t think BM25 cuts it. I don’t think anything that samples tokens based on BM25 weights (the idea in this subthread) would cut it.
"I was wondering if anyone has tried setting importance of a token as a TF-IDF or BM25 lookup."
So, I take it back. This is not a confusion. You are right to call it out. :)
I like this idea directionally. A lot of energy (literally) would be saved if we could get to the model accuracy outcomes with static weights like this.
However, I do think that this (as stated in your original message) would not work as well as transformer or SSM and I explained my reasoning as to why, already. I don't have an empirical proof (not having run the experiment) but if you believe in it, you should try it and share your findings.