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.
You ain’t seen nuthin’ yet…
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?
> Text Embeddings Reveal (Almost) As Much As Text. ... We find that although a naïve model conditioned on the embedding performs poorly, a multi step method that iteratively corrects and re embeds text is able to recover 92% of 32-token text inputs exactly. We train our model to decode text embeddings from two state of the art embedding models, and also show that our model can recover important personal information (full names) from a dataset of clinical notes.