arXiv (All)
open access
Do LLMs Really Forget? Hidden-State Leakage in Model Unlearning and How to Fix it
Unlearning in large language models (LLMs) is typically evaluated at the output level, where a model appears to suppress sensitive or undesirable content. In this work, we show that such evaluations can create an illusion of forgetting: even when output-level leakage is eliminated, sensitive informa
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
- Forgetting Only What Matters: Layer-Selective Unlearning toward Robust LLMs#997322
- Backdoor Containment via Expert Quarantine and Shutdown in LLMs#1155598
- Uncheatable Eval: Dynamic Compression-Based Evaluation of Language Models#1064054
- Limits of Reliability and Scaling in Language Models#973274
- LoRAScan: Detecting Backdoor Prompts in Low-Rank Adapters for Large Language Models via Down-Projection Activation Spike#463878
Record · ID 1131813
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