GiLT augments Transformers with semantic dependency graphs by modulating attention to improve syntactic generalization while keeping perplexity competitive and enabling better finetuning on downstream tasks.
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Existing LLM watermarking schemes can be defeated by semantic-preserving attacks including lexical changes, machine translation, and neural paraphrasing.
citing papers explorer
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GiLT: Augmenting Transformer Language Models with Dependency Graphs
GiLT augments Transformers with semantic dependency graphs by modulating attention to improve syntactic generalization while keeping perplexity competitive and enabling better finetuning on downstream tasks.
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Vaporizer: Breaking Watermarking Schemes for Large Language Model Outputs
Existing LLM watermarking schemes can be defeated by semantic-preserving attacks including lexical changes, machine translation, and neural paraphrasing.