  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.
