arXiv (All)
open access
TRACE: Data-Free Text Reconstruction Attacks against Approximate Unlearning in LLMs
As large language models (LLMs) face growing demands for data removal driven by regulatory compliance, copyright concerns, and privacy protection, approximate unlearning has emerged as a practical alternative to expensive full retraining. In practice, pre- and post-unlearning model snapshots are oft
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
- TRACE: Data-Free Text Reconstruction Attacks against Approximate Unlearning in LLMs#1442303
- Forgetting Only What Matters: Layer-Selective Unlearning toward Robust LLMs#997322
- Towards Privacy-Preserving Large Language Model: Text-free Inference Through Alignment and Adaptation#985042
- Alignment Whack-a-Mole : Finetuning Activates Verbatim Recall of Copyrighted Books in Large Language Models#1123051
- PreUnlearn: Auditing Collateral Knowledge Damage Before Large Language Model Unlearning#1051475
Record · ID 1447362
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