[Der_Einzige]

Actually, yes I am pointing out low hanging fruit here. These approaches do not have "profound consequences" for inference or training performance. In fact, sentence transformer models run orders of magnitude more quickly. Performance penalties will be small.

Also, I actually have several top NLP conference publications, so I'm not some charlatan when I say these things. I've actually physically used and seen these techniques improve LLM recall. It really actually works.

Here's more examples of low hanging fruit. The proof in that they work is in the implementations which I provide. You can run them, they work!: https://gist.github.com/Hellisotherpeople/45c619ee22aac6865c...

Check yourself before you try to check others.

[a_wild_dandan]

> In fact, sentence transformer models run orders of magnitude more quickly. Performance penalties will be small.

They do not. Sentence transformers aren't new, and have well-known trade offs. What source or line of reasoning misled you to believe otherwise?

> Here's more examples of low hanging fruit. The proof in that they work is in the implementations which I provide. You can run them, they work!: https://gist.github.com/Hellisotherpeople/45c619ee22aac6865c...

This...is your blog about prompt engineering. What do you believe this "proves"? How have you blown away current production encoding or attention mechanisms?

[pbronez]

Concur. LLM are still very young. We’re barely a year out from the ChatGPT launch. Everyone is iterating like mad. Several stealth companies working on new approaches with the potential to deliver performance leaps.

You ain’t seen nuthin’ yet…