Leading multi-bit watermarking methods for language models encode messages by biasing the model's next-token probabilities, creating a trade-off between message recovery and text quality. Their decoders typically return the highest-scoring candidate from accumulated token-level evidence, without a certified abstention...
Pawel Batorski, P. Spurek, Paul Swoboda· 0 citations
This work proposes a novel one-shot unlearning approach, abandoning iterative optimization in favor of a direct, exact analytical solution, and achieves state-of-the-art forgetting-utility trade-offs on TOFU-5%, TOFU-10%, MUSE-Books, MUSE-News and WMDP, significantly reducing computational overhead without sacrificing...
Pawel Batorski, P. Spurek, Paul Swoboda· 1 citation
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