arXiv (All)
open access
The Unequal Influence of Bad Advice: Using Training Data Attribution to Modulate Emergent Misalignment
Fine-tuning large language models on narrow, misaligned tasks can undo their post-training alignment and induce novel misaligned behaviors -- a phenomenon known as \emph{emergent misalignment} (EM). EM has been linked to persona-like representations, where fine-tuning might reduce loss by amplifying
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
- TAME: Token Attribution and Masking for Emergent misalignment#1122758
- The Unequal Influence of Bad Advice: Using Training Data Attribution to Modulate Emergent Misalignment#1122346
- Reinforcement Learning Can Amplify Emergent Misalignment from Harmless Rewards#800052
- Emergent Misalignment Is Not Magical#793339
- Emergent Misalignment Is Not Magical#789335
Record · ID 1142219
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