Multilingual != Multicultural: Evaluating Gaps Between Multilingual Capabilities and Cultural Alignment in LLMs

• URL: http://arxiv.org/abs/2502.16534v1
• Date: Sun, 23 Feb 2025 11:02:41 GMT
• Title: Multilingual != Multicultural: Evaluating Gaps Between Multilingual Capabilities and Cultural Alignment in LLMs
• Authors: Jonathan Rystrøm, Hannah Rose Kirk, Scott Hale,
• Abstract summary: Large Language Models (LLMs) are becoming increasingly capable across global languages.<n>However, the ability to communicate across languages does not necessarily translate to appropriate cultural representations.<n>We compare two families of models: Google's Gemma models and OpenAI's turbo-series.<n>We find no consistent relationships between language capabilities and cultural alignment.
• Score: 2.5212698425008377
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) are becoming increasingly capable across global languages. However, the ability to communicate across languages does not necessarily translate to appropriate cultural representations. A key concern is US-centric bias, where LLMs reflect US rather than local cultural values. We propose a novel methodology that compares LLM-generated response distributions against population-level opinion data from the World Value Survey across four languages (Danish, Dutch, English, and Portuguese). Using a rigorous linear mixed-effects regression framework, we compare two families of models: Google's Gemma models (2B--27B parameters) and successive iterations of OpenAI's turbo-series. Across the families of models, we find no consistent relationships between language capabilities and cultural alignment. While the Gemma models have a positive correlation between language capability and cultural alignment across languages, the OpenAI models do not. Importantly, we find that self-consistency is a stronger predictor of multicultural alignment than multilingual capabilities. Our results demonstrate that achieving meaningful cultural alignment requires dedicated effort beyond improving general language capabilities.

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