arXiv (OAI Expanded)
open access
Alignment Whack-a-Mole : Finetuning Activates Verbatim Recall of Copyrighted Books in Large Language Models
Frontier LLM companies have repeatedly assured courts and regulators that their models do not store copies of training data. They further rely on safety alignment strategies via RLHF, system prompts, and output filters to block verbatim regurgitation of copyrighted works, and have cited the efficacy
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
- Towards Privacy-Preserving Large Language Model: Text-free Inference Through Alignment and Adaptation#985042
- Multilingual Safety Signals Are Multi-Layered: Filtering Safety-Degrading Data for Safer LLMs#1036827
- A Gravitational Interpretation of Safety Reversion under Fine-Tuning#1129317
- RAG-Safety-Bench: Reliable Evaluation of Retrieval-Augmented LLM Safety#999446
- TRACE: Trajectory-Based Safety Patch Learning for LLM Post-Training Realignment#1118654
Record · ID 1123051
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