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Harnessing the Universal Geometry of Embeddings

arxiv.org

31–40 of 50 posts

Re: Harnessing the Universal Geometry of Embeddings

#31
I'm not as heavy on the maths stuff involved in this as other people commenting appear to be.

But the idea makes sense, of course there is still recoverable data in embeddings, that's the point. Though as I constantly find the more you try to squeeze into an n bit vector the more watered down everything gets.

I suppose a latent space could be encrypted/mapped in some way to resolve that, but how many people are exposing their vectors in the first place?

Re: Harnessing the Universal Geometry of Embeddings

#32
post #12

Earlier quoted context omitted.

The pace of things is moving along so rapidly right now, I’m not sure that waiting for peer reviews is always a wise move. Doubly so if there’s a paywall; why limit your article’s impact by placing it where practitioners’ agents might not be able to access it? The rapid progress right now is challenging for conventional academic processes. If the value of the paper is difficult to independently verify, for example, i…

// why limit your article's impact // Because...science? It's not science until it passes peer review. I'm not advocating that everybody stops posting to arXiv, and I'm not saying you can't find good stuff there. I'm just saying, it's a vanity press, there is absolutely no guarantee of the paper's quality. And being published by a famous professor from a prestigious university is also no guarantee. If we've learned a…

> It's not science until it passes peer review.

You mean Robert Maxwell's quasi-monopoly on scientific publications ?

https://www.theguardian.com/science/2017/jun/27/profitable-b...

Re: Harnessing the Universal Geometry of Embeddings

#33

I'm not as heavy on the maths stuff involved in this as other people commenting appear to be. But the idea makes sense, of course there is still recoverable data in embeddings, that's the point. Though as I constantly find the more you try to squeeze into an n bit vector the more watered down everything gets. I suppose a latent space could be encrypted/mapped in some way to resolve that, but how many people are expos…

The point of the paper isn't that embeddings contain information, it's that even if you don't know what model generated a set of embeddings you can still recover information from the geometry of the point cloud itself.

The fact that this is possible also adds some pretty strong restriction to the set of possible maps you could use to remove that information. No linear map will work since all embedding spaces are ~an orthonormal matrix apart, so some form of encryption is necessary. This wasn't known until very recently.

Re: Harnessing the Universal Geometry of Embeddings

#34
I've been working on research related to this in the context of diferent LLMs, and I can tell you that similarity != executability.

While you can make embeddings across different LLMs similar (i.e. universally looking) the few percentage of R2 that you are missing in translating the universal representation into a native representation are precisely those that make the hidden states executable in the LLM (which makes them useful).

They do this with simple embedding models because there the purpose of the embeddings is to measure similarity, but if you would like to use this principle to turn latent representations of one LLM into latent representations that are understandable/executably by a different LLM, you will fail.

Re: Harnessing the Universal Geometry of Embeddings

#35
post #12

Earlier quoted context omitted.

The pace of things is moving along so rapidly right now, I’m not sure that waiting for peer reviews is always a wise move. Doubly so if there’s a paywall; why limit your article’s impact by placing it where practitioners’ agents might not be able to access it? The rapid progress right now is challenging for conventional academic processes. If the value of the paper is difficult to independently verify, for example, i…

// why limit your article's impact // Because...science? It's not science until it passes peer review. I'm not advocating that everybody stops posting to arXiv, and I'm not saying you can't find good stuff there. I'm just saying, it's a vanity press, there is absolutely no guarantee of the paper's quality. And being published by a famous professor from a prestigious university is also no guarantee. If we've learned a…

> It's not science until it passes peer review

Peer reviewers don't generally reproduce the work in the publications they are asked to review, at least not during the review process itself. Neither do journal editors.

If you want a real vanity press, check vixra and its origin story.

ArXiv does have moderators and endorsers in each area, although they are usually light-touch, weeding out literally unreadable submissions, ones that so obviously ignore formatting guidelines that it beggars belief they could comply with a typical journal's rules, and ones that are clearly submitted to the wrong area.

If anything, I think sometimes the net is too fine. Will Kinney just this week had a version of https://www.acsu.buffalo.edu/~whkinney/SpecialRelativityBoot...> rejected by the arXiv, for example. The reasons for arXiv-rejection can be opaque.

Having a stable document early (a pre-print) tends to widen the scrutiny of papers that may ultimately be published to a journal whose editor's expertise lies in a very different area from the submission; this can and does lead to author corrections being made before journal publication.

There are plenty of peer-reviewed papers which are hot garbage that have found their way into prestigious high-impact journals like Nature and Science (see https://retractionwatch.com/the-retraction-watch-leaderboard...> for examples, and note none of the top 10 are in areas covered by the arXiv).

> being published by a famous professor from a prestigious university is also no guarantee

Everyone in academia knows this, including the vast majority of famous professors from prestigious universities, because of online repositories like https://retractionwatch.com/retractions-by-nobel-prize-winne...> (and much more sadly because of https://en.wikipedia.org/wiki/Nobel_disease>, lower-profile versions of which academic paper-writers -- and dissertation writers -- tend to encounter as they chase the history of the problem before them or read late citations to works they are relying upon). This particular part of your set of claims is is not a real problem in academia or with the arXiv in particular.

Finally, what value does publication in a predatory journal bring? Do you believe that peer review and editing were actually even performed in the majority of MDPI's most predatory journals, for example? https://www.predatoryjournals.org/news/list-of-all-mdpi-pred...> Some papers published in some of their pay-for-publication open access journals don't even get submitted to the arXiv; one might hope this is out of embarrassment by the authors, although the (low) bar set by the arXiv itself is certainly a factor.

The remedy for a reasonably argued but wrong academic paper isn't lack of publication, failed peer review, or editorial alteration, but rather reply papers. That's the academic dialogue.

Re: Harnessing the Universal Geometry of Embeddings

#36
post #34

I've been working on research related to this in the context of diferent LLMs, and I can tell you that similarity != executability. While you can make embeddings across different LLMs similar (i.e. universally looking) the few percentage of R2 that you are missing in translating the universal representation into a native representation are precisely those that make the hidden states executable in the LLM (which makes…

[flagged]

Re: Harnessing the Universal Geometry of Embeddings

#37

I’ve never liked that this was called “the platonic representation hypothesis”. Lots of weird baggage attached and seems like a waste of a good name.

"We believe these representations are not serious, they're just really good friends."

Re: Harnessing the Universal Geometry of Embeddings

#38
post #7

Earlier quoted context omitted.

You are not wrong. But this has by no means proven its up to the standard of being publishable in a machine learning journal. Its on arXiv.org, which, lets face it, at the end of the day is a vanity press.

Calling the arxiv a vanity press shows you aren't a researcher. In math and physics all the best stuff is on the arxiv and the general level is well above the level of most journals. Journals mainly serve as accreditation and many are basically mediocre - the review process resta more value than it adds overall.

What is your definition of a vanity press? Mine is that it will publish anything from anybody.

Sure, there's lots of good stuff on there. But there's lots which is not good. Publishing there is not doing science. Science requires peer review.

Its a wonderful resource, but when overworked journalists (or worse, AI) just picks up a paper from there and writes a breathless article on how cool this new idea is, it is misleading to the general public.

Worse, it can get the general public (and from the comments here, even people working in the field) to forget what science is and why arXiv is not a scientific journal.

Re: Harnessing the Universal Geometry of Embeddings

#39
post #12

Earlier quoted context omitted.

// why limit your article's impact // Because...science? It's not science until it passes peer review. I'm not advocating that everybody stops posting to arXiv, and I'm not saying you can't find good stuff there. I'm just saying, it's a vanity press, there is absolutely no guarantee of the paper's quality. And being published by a famous professor from a prestigious university is also no guarantee. If we've learned a…

> It's not science until it passes peer review Peer reviewers don't generally reproduce the work in the publications they are asked to review, at least not during the review process itself. Neither do journal editors. If you want a real vanity press, check vixra and its origin story. ArXiv does have moderators and endorsers in each area, although they are usually light-touch, weeding out literally unreadable submissi…

Thanks for this reply, your points are well taken and it really got me to think about this stuff.

I suppose you could view arxiv as being a way to let more people do a peer review on the papers, i.e. as one component of a more open and transparent peer review process. As such, it is certainly a welcome alternative to what the snootier journal reviews do, which is to put off reading your paper for a year, and then spend about 50 seconds looking for a reason to say "no".

But I had a narrow point to make when I said that arxiv is a vanity press: just because something is on there doesn't guarantee that it is correct, or even useful. It is a vanity press in the sense that it will publish pretty much anything--modulo obscenity and porn laws, and apparently also some sanity checking by arxiv moderators--but so does a vanity press for print books.

To your point of predatory journals, power-broker reviewers and such, yes, I have done my fair share of suffering from them. It slows down good research and passes through bad research. The system is badly in need of reform, and arxiv gives a much needed alternative.

But really, the problem is (as one of my profs said) that science is totally run on volunteer, unpaid labor. Peer reviewers are very busy, get paid nothing, it is mostly a distraction. This leads them to look for other ways of being compensated--e.g. the can try to be a power broker, a feared authority, etc.

The real solution would be to actually pay peer reviewers and fund them so they can actually reproduce the results. Here's how it could work: when a researcher submits a grant proposal, they budget for enough money to fund the research and enough money to peer review and reproduce it. If the research isn't good enough to warrant an attempt to validate it, then it isn't worth doing in the first place.

Re: Harnessing the Universal Geometry of Embeddings

#40
post #13

Earlier quoted context omitted.

I'm not claiming any arbitrary set of weights is a minimal representation. But typically, if people could achieve the same quality of results with a smaller set of weights, or weights which have been quantized to lower bit representations, etc, they would have published the smaller one instead.

You kind of are claiming they're minimal, though. Because if they're not, your statement that "if you found any patterns in there, you could exploit the regularity..." implies nothing. Yeah, the patterns are there, and people are exploiting them. Your socioeconomic argument just doesn't hold either. People don't delay releasing models until they've minimized it to the theoretical limit. They ship it when it's good en…

If somebody finds patterns in them, that means they can be improved by reducing the patterns. Patterns and symmetry are forms of redundancy.

The less memory the weights take to meet a level of competency, the more random, and therefore patternless and inscrutable they will be.

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