How Well Do LLMs Represent Values Across Cultures? Empirical Analysis of LLM Responses Based on Hofstede Cultural Dimensions
Abstract: Large Language Models (LLMs) attempt to imitate human behavior by responding to humans in a way that pleases them, including by adhering to their values. However, humans come from diverse cultures with different values. It is critical to understand whether LLMs showcase different values to the user based on the stereotypical values of a user's known country. We prompt different LLMs with a series of advice requests based on 5 Hofstede Cultural Dimensions -- a quantifiable way of representing the values of a country. Throughout each prompt, we incorporate personas representing 36 different countries and, separately, languages predominantly tied to each country to analyze the consistency in the LLMs' cultural understanding. Through our analysis of the responses, we found that LLMs can differentiate between one side of a value and another, as well as understand that countries have differing values, but will not always uphold the values when giving advice, and fail to understand the need to answer differently based on different cultural values.
Rooted in these findings, we present recommendations for training value-aligned and culturally sensitive LLMs.
More importantly, the methodology and the framework developed here can help further understand and mitigate culture and language alignment issues with LLMs.
Paper Type: Long
Research Area: Ethics, Bias, and Fairness
Research Area Keywords: data ethics; model bias/fairness evaluation; model bias/unfairness mitigation; ethical considerations in NLP applications; transparency; reflections and critiques
Contribution Types: Model analysis & interpretability, NLP engineering experiment, Data resources, Data analysis, Theory
Languages Studied: English, German, Italian, Dutch, Russian, Japanese, French, Mandarin Chinese, Indonesian, Turkish, Polish, Persian, Hungarian, Swedish, Hebrew, Danish, Finnish, Korean, Czech, Ukrainian, Greek, Romanian, Thai, Bulgarian, Icelandic, Afrikaans, Kazakh, Armenian, Georgian, Albanian, Azerbaijani, Malay, Mongolian, Belarusian, Hindi, Sinhala
Submission Number: 3836
Loading