Averaging output distributions across 3-5 LLMs recovers the unwatermarked distribution, suppressing detection z-scores below threshold while improving quality.
A watermark for low-entropy and unbiased generation in large language models
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A topic-guided watermarking scheme partitions the LLM vocabulary into topic-aligned token subsets and green-lists relevant tokens based on the input prompt to embed detectable marks while preserving text quality and improving robustness to attacks.
The thesis presents a kernel method for multiaccuracy across overlooked subpopulations, information-theoretic optimal watermarking for LLMs, and a simulator showing LLM agents outperforming humans in supply chains while creating tail risks.
MATRIX embeds multi-layer watermarks in LLM-generated code via dual-channel constrained parity-check encoding, achieving 99.2% detection accuracy with 0-0.14% functionality loss and 7.7-26.67% better attack robustness than prior methods.
citing papers explorer
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Linear Ensembles Wash Away Watermarks: On the Fragility of Distributional Perturbations in LLMs
Averaging output distributions across 3-5 LLMs recovers the unwatermarked distribution, suppressing detection z-scores below threshold while improving quality.
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Topic-Based Watermarks for Large Language Models
A topic-guided watermarking scheme partitions the LLM vocabulary into topic-aligned token subsets and green-lists relevant tokens based on the input prompt to embed detectable marks while preserving text quality and improving robustness to attacks.
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Trustworthy AI: Ensuring Reliability and Accountability from Models to Agents
The thesis presents a kernel method for multiaccuracy across overlooked subpopulations, information-theoretic optimal watermarking for LLMs, and a simulator showing LLM agents outperforming humans in supply chains while creating tail risks.
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MATRIX: Multi-Layer Code Watermarking via Dual-Channel Constrained Parity-Check Encoding
MATRIX embeds multi-layer watermarks in LLM-generated code via dual-channel constrained parity-check encoding, achieving 99.2% detection accuracy with 0-0.14% functionality loss and 7.7-26.67% better attack robustness than prior methods.