The feature itself is something I wanted to play with too, as it's kind of an obvious thing to want. I mean, these models execute a pipeline:
[text] -> [tokens] -> {[embeddings] -> [inference] -> [embeddings]} -> [tokens] -> [text]
Where the part in { ... } may or may not be implemented as a single step (i.e. all three parts interleaved).
Now, apparently all the magic[0] of transformer models sits in the latent space and is invoked by the { ... } bit. We also know for sure that you can make the pipeline look like this:
[text] -> [tokens] -> {[embeddings] -> [inference]} -> [embeddings]
So with the two things in mind, it's kind of obvious you'd also want a pipe that looks like:
[embeddings] -> [inference] -> [embeddings] (and optionally -> [tokens] -> [text])
for the sole purpose of messing around and exploring the latent space itself.
I'm very much not up to date with the whole space, so I might be missing something, but I'd thought that poking around the latent space would be getting a lot more attention than it seems to be getting.
(EDIT: replaced < ... > with { ... } for readability.)
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[0] Not the "how do transformers tick" details, but the "how the hell are they this good" / "GPT-4 is uncanny valley" / "could this thing be actually thinking?" kind of magic.