6 ms·
Simple version: In instances wherein the otherwise statistically chosen next word is a "toss-up", watermarking removes the randomness by imposing specific choic
by unclebucknasty 1mo ago
Simple version: In instances wherein the otherwise statistically chosen next word is a "toss-up", watermarking removes the randomness by imposing specific choices, determined by a key. This then becomes a detectable pattern when scanned with the key (stastically—detection itself is probabilistic).
>use it to store arbitrary information
No additional data is embedded. The range of available data is constrained by the text being generated (i.e. the sets of "next words" per text).
- mbreese 1mo agoFrom what I’ve read, they won’t be imposing specific choices, but using a different (biased) RNG for those “toss-up” choices. With enough sampling, you could detect if the RNG was biased or not.
- unclebucknasty 1mo agoThis is what I meant by "imposing specific choices, determined by a key". Maybe "impose" or "specific" were too strong in my attempt to simplify? I attempted to clarify that the impositions themselves are not deterministic, by indicating that the entire process is still probabilistic. Maybe Anthropic's explanation is simple enough [0]: >When watermarking is used, choices are still made at random, but the source of the randomness is different. Instead of using an arbitrary random number generator to pick the next word, watermarking uses the key and a few words that come before to settle what word the model should pick. That is, the words that Claude picks are still random, but now, one can check the sequence of words and see if it’s consistent with the choices Claude would make if it was using the key. If it is, one can assign a probability that the text was generated by Claude. [0] https://www.anthropic.com/news/claude-text-watermark https://www.anthropic.com/news/claude-text-watermark