arXiv (All)
open access
Towards Mitigating Excessive Forgetting in LLM Unlearning via Entanglement-Guidance with Proxy Constraint
Large language models (LLMs) are trained on massive datasets that may include private or copyrighted content. Due to growing privacy and ownership concerns, data owners may request the removal of their data from trained models. Machine unlearning provides a practical solution by removing the influen
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
- Removing the NEEDLE in the Haystack: Backdoor Removal in LLMs via Weight Orthogonalisation#1154971
- Backdoor Containment via Expert Quarantine and Shutdown in LLMs#1155598
- P2Skill: Privacy Preserving Skill Distillation for Cloud-Local LLM Inference Systems#458232
- Confess What You Know: Forget-Set Misalignment with Model Knowledge in LLM Unlearning#820032
- Machine unlearning using forgetting neural networks#405948
Record · ID 1052017
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