arXiv (All)
open access
Robust Text Watermarking for Large Language Models via Dual Semantic Embeddings
This work presents Dual-Embedding Watermarking (DEW), a semantic watermarking scheme for large language models (LLMs) that leverages contextual and token-level embeddings to enhance robustness against paraphrasing and translation. DEW utilizes a signal-processing methodology, applying algebraic vect
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Robust Text Watermarking for Large Language Models via Dual Semantic Embeddings#324824
- Counting and Min-Cost Encoding for Tokenization in Large Language Models#1156062
- AliMark: Enhancing Robustness of Sentence-Level Watermarking Against Text Paraphrasing#238522
- Resolution as a First-Class Decision: Task-Conditioned Routing for Efficient Multimodal Large Language Models#1129550
- Selecting What Matters: Semantic Compression-Guided Selective Pooling for Long-Context Embeddings#1142091
Record · ID 927363
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive