arXiv (All)
open access
UNLEARN Efficient Removal of Knowledge in Large Language Models
Given the prevalence of large language models (LLMs) and the prohibitive cost of training these models from scratch, dynamically forgetting specific knowledge e.g., private or proprietary, without retraining the model has become an important capability. This paper proposes a novel method to achieve
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
- Specializing Without Forgetting: Analyzing Knowledge Preservation in Multilingual Model Adaptation#1186477
- A Deafening Silence: Catastrophic Forgetting Lives in the Output Embeddings of Tokens the Data Never Speaks#1234202
- Realistic Continual Learning Approach using Pre-trained Models#1083966
- PreUnlearn: Auditing Collateral Knowledge Damage Before Large Language Model Unlearning#1051475
- CRAFT: Forgetting-Aware Intervention-Based Adaptation for Continual Learning#1355573
Record · ID 1356988
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