Scenario-based Probing and Steering Cultural Values in Large Language Models--Extended Version
Large Language Models (LLMs) are deployed across cultural contexts but often reflect homogenized values inherited from training data. Evaluations of cultural alignment typically rely on direct prompting with survey-style questions, which frequently elicit neutral or safety-aligned responses and fail to capture underlying model preferences. We propose a framework for probing and steering latent cultural representations in LLMs along the two Inglehart--Welzel axes of the World Values Survey (WVS). By translating social value questions into scenario-based behavioral dilemmas, we extract token-level probabilities to measure implicit values and apply activation steering, optionally combined wi...
| Authors | Trung Duc Anh Dang, Tung Kieu, Sarah Masud |
|---|---|
| Primary category | cs.CL |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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