Prompt Framing Substantially Alters Cultural Alignment in Large Language Models
A new arXiv preprint investigates whether different prompt framings—personalization, persona role-play, and third-person forecasting—produce interchangeable results when aligning large language models (LLMs) with human cultural values. Testing four major LLMs across 13 language-country contexts using World Values Survey questions, the study finds that prompt framing significantly shifts model responses, with third-person forecasting generally producing the closest alignment to human values. The findings indicate that prompt framing is a key factor in how LLMs express cultural alignment, not merely a superficial choice.
Why it matters: This work highlights that the way LLMs are prompted can fundamentally alter their alignment with human values, which is important for deploying AI systems in diverse cultural settings.
Full story at: arXiv AI/ML ↗