title: How Well Do LLMs Represent Values Across Cultures? Empirical Analysis of
    LLM Responses Based on Hofstede Cultural Dimensions
summary: 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_authors._id: 6678d17ab31a31445b4a81a3
paper_authors.name: Julia Kharchenko
paper_authors.status: admin_assigned
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paper_authors.fullname: Julia Kharchenko
paper_authors.type: user
paper_authors.user: juliakharchenko
paper_authors._id: 6678d17ab31a31445b4a81a4
paper_authors.name: Tanya Roosta
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-06-24T01:53:01.451Z
paper_authors._id: 614ca5186fe73d23394ec4da
paper_authors.avatarUrl: /avatars/2b53e63f8e2ff70243f2f6596bc1e497.svg
paper_authors.fullname: Tanya Roosta
paper_authors.type: user
paper_authors.user: tanyaroosta
paper_authors._id: 6678d17ab31a31445b4a81a5
paper_authors.name: Aman Chadha
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-24T07:29:30.154Z
paper_authors._id: 63a4754927f1f64ed7238dac
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paper_authors.fullname: Aman Chadha
paper_authors.type: user
paper_authors.user: amanchadha
paper_authors._id: 6678d17ab31a31445b4a81a6
paper_authors.name: Chirag Shah
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-25T07:47:43.218Z
paper_authors._id: 6624485b0e31d65ecce09748
paper_authors.avatarUrl: /avatars/b78085aa72e673e570843b85b116f8bc.svg
paper_authors.fullname: Chirag Shah
paper_authors.type: user
paper_authors.user: chiragshah
paper_title: How Well Do LLMs Represent Values Across Cultures? Empirical Analysis of
    LLM Responses Based on Hofstede Cultural Dimensions
paper_summary: 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_ai_summary: LLMs can differentiate cultural values but do not always provide culturally aligned advice, suggesting need for improved cultural and language sensitivity training.
paper_ai_keywords: Large Language Models Hofstede Cultural Dimensions