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Really inspirational project. Does anyone know if there's an analog of this to the LLM space? I.e. can you take the embedding of two sentences and get a 'combined' sentence? Either by searching a corpus for the closest match or by feeding that combined embedding into an LLM that generates it.

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A recent paper [1] shows that what you're describing is possible to some degree - you can reproduce text from its embeddings. The paper provides the code implementing the reversal process, so you could quickly hack together a prototype :)

I also recommend a video by Yannic Kilcher [2], explaining the paper.

[1] https://arxiv.org/abs/2310.06816

[2] https://www.youtube.com/watch?v=FY5j3P9tCeA

isn't that how translation works in tranformers? encoder produces embedding then decoder generates the sentence. do it again with reverse translation and you get your text back in original language. combining two sentences can be done by using model for summary. making a cross-breed of two sentences is a different problem.

It seems to me like a lot of what LLMs and Image-generators do is find the interpolation two points in concept-space. So that's not vector-addition but vector-averaging. It's still arithmetic.

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