arXiv (All)
open access
Unmasking the Secret Code: Using Canaries to Show How Masked Context Controls Memorization in SFT
Canaries, or synthetic training records containing known secrets, are widely used to assess whether language models reveal information from their training data. But how much do these measurements depend on the canaries themselves? We study this question through controlled fine-tuning experiments wit
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
- PrivDrift: Auditing User-Secret Leakage Under Topic Drift in Active LLM Conversations#1078165
- Conditional Transfer from Controlled Pretraining Mixtures to Code#1248828
- Demystifying the Privacy-Utility Trade-off in LLM Interactions#1069950
- On the Recoverability of Private Information Unlearning in Large Language Models#799191
- On the Recoverability of Private Information Unlearning in Large Language Models#797768
Record · ID 1355645
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