Personalization, Personas, and Forecasting in Value Alignment
LLM behavior may be conditioned by human identity in several ways: they may be asked to adapt to users, role-play populations, or forecast how people would answer value-laden questions. We test whether these framings are interchangeable using the World Values Survey (WVS). We evaluate GPT-5.4, Claude Sonnet 4.6, Gemini 2.5 Flash, and Qwen3-235B on 101 WVS-derived questions across 13 language-country slices, comparing a language-only baseline with user-country, persona-country, and third-person prompts. Across 21,008 model-response rows, prompt framing is a first-order determinant of cultural alignment: country cues often shift answers substantially, but not all shifts move toward matched ...
| Authors | James Wedgwood, Pratiksha Thaker, Neil Kale et al. |
|---|---|
| Primary category | cs.AI |
| Published | 21 June 2026 |
| Last updated at source | 21 June 2026 |
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