arXiv (All)
open access
How LLMs Are Persuaded: A Few Attention Heads, Rerouted
Large language models can answer a factual question correctly, yet switch to an incorrect answer when exposed to persuasive context. Why this happens inside the model remains unclear. In this work, we study this question through causal interventions in controlled multiple-choice tasks and identify t
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
- A light-touch AI literacy intervention helps protect against AI political persuasion#1014850
- The Thin Line Between Comprehension and Persuasion in LLMs#153094
- Moloch's Bargain: Emergent Misalignment When LLMs Compete for Audiences#1156216
- Choices Speak Louder than Questions#1131383
- Adaptive Multi-Value Control in LLMs via Causal Activation Steering#1104604
Record · ID 1128593
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