[heavyarms]

I've been thinking along the same lines. The token window IMO should be a conceptual inverted pyramid, where there most recent tokens are retained verbatim but previous iterations are compressed/pooled more and more as the context grows. I'm sure there's some effort/research in this direction. It seems pretty obvious.

[matsemann]

But some of the earlier tokens are also the most important ones, right? Like the instructions and rules you want it to follow.

[visarga]

Phrase embeddings could bring a 32x reduction in sequence length because:

> 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.

https://arxiv.org/abs/2310.06816

[a_wild_dandan]

They are. Moreover, the idea that AI companies are missing and/or not implementing this “obvious” tactic is hilarious. Folks, these approaches have profound consequences for training and inference performance. Y’all aren’t pointing out some low hanging fruit here, lol

[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…