> When a supported Claude model generates text, it weaves an imperceptible watermark directly into the text itself. You won’t see it, and it doesn’t change the meaning, quality, or readability of Claude’s response. I'd like to know a lot more about how that works. A lot of my interactions with Claude return pretty precise text. If I ask it to edit a project and refactor a specific function in several places I know ex…
You can carefully select which pseudorandom number generator (prng) you use to be able to id text of a certain length. I expect there is some performance characteristic you have to manage since you're doing this on every inference, but once you do that it doesn't change the output in any meaningful way (the prng is still a statistically valid prng, it just happens to let you check if the output used that prng)
The point is that you can do this simply by swapping to a different RNG, which isn't noticeable to the end user, and while it changes the output, it's not any different from how using a different seed or being lumped in a different batch will change the output.
^ excerpt:
> So then to watermark, instead of selecting the next token randomly, the idea will be to select it pseudorandomly, using a cryptographic pseudorandom function, whose key is known only to OpenAI. That won’t make any detectable difference to the end user, assuming the end user can’t distinguish the pseudorandom numbers from truly random ones. But now you can choose a pseudorandom function that secretly biases a certain score—a sum over a certain function g evaluated at each n-gram (sequence of n consecutive tokens), for some small n—which score you can also compute if you know the key for this pseudorandom function.