SWAN uses AMR to embed semantic watermarks that persist through paraphrases, matching SOTA detection on original text and improving AUC by 13.9 points on paraphrased RealNews data.
Watermarking pre- trained language models with backdooring
4 Pith papers cite this work. Polarity classification is still indexing.
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A survey of LLM copyright protection that unifies text watermarking, model watermarking, and model fingerprinting while presenting new coverage of fingerprint transfer and removal.
PAR fine-tunes CLIP to remove backdoors from structured triggers while preserving standard performance, and works even with only synthetic image-text pairs.
P2F generates low-rank parameter increments for LLM fingerprinting directly from textual descriptions in a single forward pass.
citing papers explorer
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SWAN: Semantic Watermarking with Abstract Meaning Representation
SWAN uses AMR to embed semantic watermarks that persist through paraphrases, matching SOTA detection on original text and improving AUC by 13.9 points on paraphrased RealNews data.
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Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends
A survey of LLM copyright protection that unifies text watermarking, model watermarking, and model fingerprinting while presenting new coverage of fingerprint transfer and removal.
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Perturb and Recover: Fine-tuning for Effective Backdoor Removal from CLIP
PAR fine-tunes CLIP to remove backdoors from structured triggers while preserving standard performance, and works even with only synthetic image-text pairs.
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Prompt2Fingerprint: Plug-and-Play LLM Fingerprinting via Text-to-Weight Generation
P2F generates low-rank parameter increments for LLM fingerprinting directly from textual descriptions in a single forward pass.