arXiv CS
open access
Guided Diffusion for Trustworthy Synthetic Text Generation
The rapid evolution of Large Language Models is fueled by massive datasets, yet this reliance poses severe privacy risks, as models can inadvertently memorize and even directly output sensitive training data. Synthetic text generation offers a promising solution to this challenge; however, existing
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
- Guided Diffusion for Trustworthy Synthetic Text Generation#1402647
- Adversarial Reframing: A Framework for Targeted Generation in Language Models#216742
- Anchored or Drifting: What Recursive Self-Generation Reveals About Training Data#1202131
- Efficient Guided Generation for Large Language Models#305584
- Provable Training Data Identification for Large Language Models#1402976
Record · ID 1394181
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