Hey, I read the paper in detail and presented to colleagues during our reading group.
I still do not understand exactly where D1L comes from in LGan(D1L, T(A1(u)). Is D1L simply A1(u)?
I also find that mixing notation in figure 2 and 3 makes it tricky.
Would have loved to have more insights from the results in the tables.
And more results from inversion, on more than Enron dataset. Since that is one end goals, even if reusing another method.
Thank you for the paper, very interesting!
The fact that embeddings from different models can be translated into a shared latent space (and back) supports the notion that semantic anchors or guides are not just model-specific hacks, but potentially universal tools. Fantastic read, thank you.
Given the demonstrated risk of information leakage from embeddings, have you explored any methods for hardening, obfuscating, or 'watermarking' embedding spaces to resist universal translation and inversion?
I don't see how the "different data" aspect is evidenced. If the "modality" of the data is the same, we're choosing a highly specific subset of all possible data -- and, in practice, radically more narrow than just that. Any sufficiently capable LLM is going to have to be trained on a corpus not-so-dissimilar to all electronic texts which exist in the standard corpa used for LLM training.
The idea that a data set is "different" merely because its some subset of this maximal corpa is a difference without a distinction. What isnt being proposed is, say, that training just on all the works of scifi fiction lead to a zero-info translatable embedding space projectable into all the works of horror, and the like (or say that english-scifi can be bridged to japanese-scifi by way of a english-japanese-horror-corpus).
The very objective of creating LLMs with useful capabilities entials an extremely similar dataset starting point. We do not have so many petabytes of training data here that there is any meaningful sense in which OpenAI uses "only this discrete subspace" and perplextiy, "yet another". All useful LLMs sample roughly randomly across the maximal corpus that we have to hand.
Thus this hype around there being a platonic form of how word tokens ought be arranged seems wholly unevidenced. Reality has a "natural arrangement" -- this does not show that our highly lossy encoding of it in english has anything like a unique or natural correspondence. It has a circumstantial correspondance in "all recorded electronic texts" which are the basis for training all generally useful LLMs.
This arguably means that we could translate any unknown alien message if it is sufficiently long and not encrypted: 1) Create embeddings from the Alienese message. 2) Convert them to English.
Could we also use this to read the Voynich manuscript, by converting Voynichese into embeddings and embeddings into English text? Perhaps, though I worry the manuscript is too short for that.
Very cool! I've been looking for something like this for a while and couldn't find anyone doing it. I've been investigating a way to translate LoRAs between models and this seems like it could be a first step towards that.
Huh. So Plato was right. This has many implications for philosophy. Interestingly, the 12th century Platonic-influenced Arab philosopher Ibn Arabi described methods of converting text to numbers (embeddings) and then performing operations on those numbers to yield new meanings (inference). A 12th century LLM? His books are full of these kinds of operations (called Abjad math) and a core part of his textual hermeneutics.
Can this be used to allow different embedding models to communicate with each other in embedding space?
Very cool! Do you think if an alien civilization created an embedding model for their alien corpus of text it would satisfy this?
This seems like a catastrophe in the wings for legal-services-RAG companies.
Hi HN, I'm Jack, the last author of this paper. It feels good to release this, the fruit of a two-year quest to "align" two vector spaces without any paired data. It's fun to look back a bit and note that at least two people told me this wasn't possible:
1. An MIT professor who works on similar geometry alignment problems didn't want to work on this with me because he was certain we would need at least a little bit of paired data
2. A vector database startup founder who told me about his plan to randomly rotate embeddings to guarantee user security (and ignored me when I said it might not be a good idea)
The practical takeaway is something that many people already understood, which is that embeddings are not encrypted, even if you don't have access to the model that produced them.
As one example, in the Cursor security policy (https://www.cursor.com/security#codebase-indexing) they state:
> Embedding reversal: academic work has shown that reversing embeddings is possible in some cases. Current attacks rely on having access to the model [...]
This is no longer the case. Since all embedding models are learning ~the same thing, we can decode any embedding vectors, given we have at least a few thousand of them.