jackpirate

Sorry if I'm being obtuse, but I don't see any mention of the POT package in your paper or of what specific algorithms you used from it to compare against. My best guess is that you used the linear map similar to the example at <https://pythonot.github.io/auto_examples/domain-adaptation/p...>. The methods I mentioned are also linear, but contain a number of additional tricks that result in much better performance than a standard L2 loss, and so I would expect those methods to outperform your OT baseline.

> As for the name – the paper you recommend is called 'vecmap' which seems equally general, doesn't it? Google shows me there are others who have developed their own 'vec2vec'. There is a lot of repetition in AI these days, so collisions happen.

But both of those papers are about generic vector alignment, so the generality of the name makes sense. Your contribution here seems specifically about the LLM use case, and so a name that implies the LLM use case would be preferable.

I do agree though that in general naming is hard and I don't have a better name to suggest. I also agree that there's lots of related papers, and you can't cite/discuss them all reasonably.

And I don't mean to be overly critical... the application to LLMs is definitely cool. I wouldn't have read the paper and written up my critiques if I didn't overall like it :)

Naively, I would expect off-the-shelf embedding alignment algorithms (like <https://github.com/artetxem/vecmap> and <https://github.com/facebookresearch/fastText/tree/main/align...>, neither of which are cited or compared against) to work quite well on this problem. So I'm curious if they don't or why they don't.

I can imagine there is lots of room for improvements around implicit regularization in the algorithms. Specifically, these algorithms were designed with word2vec output in mind (typically 300 dimensional vectors with 200000 observations), but your problem has higher dimensional vectors with fewer observations and so would likely require different hyperparameter tuning. IIRC, there's no explicit regularization in these methods, but hyperparameters like stepsize/stepcount can implicitly add L2 regularization, which you probably need for your application.

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PS.

I *strongly dislike* your name of vec2vec. You aren't the first/only algorithm for taking vectors as input and getting vectors as output, and you have no right to claim such a general title.

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PPS.

I believe there is a minor typo with footnote 1. The note is "Our code is available on GitHub." but it is attached to the sentence "In practice, it is unrealistic to expect that such a database be available."