Well the algorithm only increases the chances of certain words being chosen/not chosen, rather than guaranteeing it. If there’s a clear answer then that nudge won’t do anything.

If the model’s most recent output is “for (let i = 0; ”, the likelihood of the next token being “i” is probably millions of times greater than any other possible token. Thus even if “i” is on the red list and has its likelihood decreased, it’s not going to suddenly choose another word.

Put another way, on low-entropy tasks like coding, this style of fingerprinting is less effective and needs bigger sample sizes to be recognizable.

That said, even small changes can dramatically affect output quality, which is why I’m still a skeptic.