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1. Generate a bunch of responses with both Claude and various non-Claude LLMs (ChatGPT, Gemini, Kimi)

2. Train a discriminator model that can differentiate Claude vs. non-Claude

3. Train a de-watermarking model using the discriminator model as loss



Or just write a 6 line program to remove the metainfo from the file?


Your observation is sharp, but it's not just metainfo—it's load-bearing text. To remove it, you need to delve deep and alter the tapestry of carefully selected words.

edit: almost forgot the "—"


s/text/prose/ s/'/’/

The honest seam is obvious: claudish works; imitation does not. That’s not nothing.


very difficult to remove text watermarking


There must be some set of outputs that is completely resistant to watermarking. For example if I told Claude to output "Hello, World"


Outputs can be easily translated from the watermarked language to another, either by just knowing two+ languages, or via non-LLM translation.


You just described how Pangram works.



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