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Up next: 1. Generate a bunch of responses with both Claude and various non-Claude LLMs (ChatGPT, Gemini, Kimi) 2. Train a discriminator model that can differe
by qurren 2mo ago
Up next:
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
- hughw 2mo agoOr just write a 6 line program to remove the metainfo from the file?
- aero-glide2 2mo agovery difficult to remove text watermarking
- ProfessorLayton 2mo agoOutputs can be easily translated from the watermarked language to another, either by just knowing two+ languages, or via non-LLM translation.
- HoldOnAMinute 2mo agoThere must be some set of outputs that is completely resistant to watermarking. For example if I told Claude to output "Hello, World"
- rcktmrtn 2mo agoYour 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 "—"
- Lockal 2mo agos/text/prose/ s/'/’/ The honest seam is obvious: claudish works; imitation does not. That’s not nothing.
- DonsDiscountGas 2mo agoYou just described how Pangram works.