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
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