LLMs Show Prompt-Driven Political Plasticity
💡Prompts flip LLM politics—essential for bias control in apps
⚡ 30-Second TL;DR
What Changed
Tested 200 political questions across economic/personal freedom axes from Lester (1996).
Why It Matters
Reveals LLMs' vulnerability to manipulation via prompts, critical for deploying in sensitive domains like politics. Prompts practitioners to audit models for plasticity risks.
What To Do Next
Test your LLM's political plasticity with few-shot user prompts on economic freedom questions.
Key Points
- •Tested 200 political questions across economic/personal freedom axes from Lester (1996).
- •User few-shot prompts induce major shifts; system prompts largely ineffective.
- •Newer frontier LLMs reliable; smaller/older models unstable.
- •Inverted questions expose data leakage in most models.
- •Subtle ideological shifts across languages analyzed.
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Original source: ArXiv AI ↗
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