arXiv (All)
open access
TULIP: Targeted LLM Unlearning at Layers Identified Per-Input
Representation-level unlearning intervenes on the intermediate hidden states of LLMs. Although knowledge is distributed across layers, existing methods operate at a single fixed layer for the entire forget set. We ask whether such a fixed layer is sufficient. To answer this, we design a hijacking ex
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
- Neuralyzing the Trace: Selective Representation-Level Unlearning with Contrastive Sparse Autoencoders#1105614
- Measuring the Depth of LLM Unlearning via Activation Patching#793684
- Measuring the Depth of LLM Unlearning via Activation Patching#789680
- Concept Score Relearning: A Unified Cross-Architecture Attack on Concept Erasure#1118157
- Learning What to Forget: Distributional Unlearning for LLM Representation Spaces#1143530
Record · ID 1128854
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