Still waiting for the day that medium term memory (token average pooling like in sentence transformers) becomes used for this. It's staring all of these companies in the face and apparently no one thinks to implement it.
> 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.
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.
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?
You ain’t seen nuthin’ yet…
For example, the researchers working hard on better text sampling techniques (i.e. https://arxiv.org/abs/2202.00666), or on better constraint techniques (i.e. like this https://arxiv.org/abs/2306.03081), or on actual negative prompting/CFG in LLMs (i.e. like this https://github.com/huggingface/transformers/issues/24536) are doing far FAR more to advance the state of AI than dozens of VC backed LLM companies operating today. They are all laboring in relative obscurity.
HN, and the NLP community have some serious blindspots with knowing how to exploit their own technology. At least someone at Andreessen Horowitz got a clue and gave some funding to Oogabooga - still waiting for Automatic1111 to get any funding.
Something interesting that I heard from people trying to memorize things better is that memory “storage space” limits for people are essentially irrelevant. We’re limited by our learning and forgetting speeds. There’s no evidence of brains getting “full”.
Think of it like a giant warehouse of plants, with one employee. He can accept shipments (learning). He can take care of plants (remembering). Too long without care and they die (forgetting). The warehouse is big enough that it is not a limiting factor in how many plants he can keep alive. If it was 10x bigger it wouldn’t make a bit of difference.
It's just that, unlike these AIs, we're capable of online learning.