Artificial Minds, Cultural Shadows: Cultural Alignment, Identity, and Voice Across Multiple Large Language Models

Abstract

Abstract Large language models (LLMs) are increasingly used not only to retrieve information but also to generate language on behalf of users, raising important questions about how they mediate cultural identity and authorial voice. This study investigates that issue through a comparative analysis of five widely used systems – ChatGPT, Claude, Copilot, Gemini, and Perplexity – using the Inglehart–Welzel cultural map as an empirical framework. The analysis is conducted in two stages: first, a quantitative assessment of cultural value alignment based on model-generated responses to standardized survey items under neutral and country-specific prompting conditions; and second, a qualitative examination of linguistic features in selected excerpts of responses to explore how voice and stance are constructed. Representational alignment is measured as the distance between model outputs and national cultural profiles. The results show that baseline responses cluster around English-speaking and European cultural regions, indicating shared default priors, while country-specific prompting improves alignment, though performance is uneven across models and persistent gaps remain in several non-Western contexts. These patterns are conceptualized as cultural shadows, reflecting the influence of dominant cultural norms in model outputs. Drawing on Hyland’s (2005) account of stance and McKinley’s (2026) view of generative artificial intelligence (GenAI) as a rhetorical force, the findings suggest that these patterns extend beyond value alignment to the mediation of voice and self-representation, with differences across models indicating that AI-generated language may shape how culturally situated perspectives are expressed.