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This paper explores how persona prompting with different attribute selection methods affects the alignment of large language models with human responses in social surveys. It finds that effectiveness depends on human response variation and the choice of attributes.
This paper tests whether different prompt framings (personalization, role-play, third-person forecasting) are interchangeable in eliciting cultural values from LLMs, using the World Values Survey. Results show that prompt framing significantly affects model responses and measured cultural alignment, with third-person forecasting yielding the strongest directional alignment.