Preventing LLM Distillation: Text Watermarking May See Real-World Use
yuxiangw_cs · x · 2026-08-11
As the risk of Large Language Models being stolen via API distillation increases, distillation-resistant text watermarking, a technique previously proposed by academia, is showing practical value.
The method protects model IP by injecting a secret-key-based watermark into the model's prediction probabilities. It can detect theft by probing a suspect model. Experiments show that this approach achieves a 100% mean average precision across multiple NLP tasks without degrading the original model's accuracy.
The author raises a question to Anthropic: will models like Claude eventually offer a "watermark-free" premium tier for specific use cases?
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