this post was submitted on 10 Sep 2023
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The output is pure text so you would have to hide the signature in the response itself. On top of being useless since most users slightly modify the text after receiving it, it would probably have a negative effect on the quality. It's also insanely complicated to train that kind of behavior into an llm.
Your implementation of my concept might be useless, but that doesn’t mean the concept is.
One possible solution would be to look at how responses are structured, letter frequencies, etc. The flexibility/ambiguous nature natural language is that you can word things in many many different ways which allows for some creative meta techniques to accomplish a fingerprint.
It is a valid idea, and not impossible. When generating text, a language model gives a list of possible tokens.. or more correctly it gives a weight to every possible token where most would be 0 weight. Then there's multiple ways to pick the next token, from always picking top one to select random from top X tokens to mirostat and so on. You could probably do some extra weighting to embed a sort of signature. At some quality loss