Cultural Value Alignment in Large Language Models: A Prompt-based Analysis of Schwartz Values in Gemini, ChatGPT, and DeepSeek

• URL: http://arxiv.org/abs/2505.17112v1
• Date: Wed, 21 May 2025 14:03:19 GMT
• Title: Cultural Value Alignment in Large Language Models: A Prompt-based Analysis of Schwartz Values in Gemini, ChatGPT, and DeepSeek
• Authors: Robin Segerer,
• Abstract summary: This study examines cultural value alignment in large language models (LLMs) by analyzing how Gemini, ChatGPT, and DeepSeek prioritize values from Schwartz's value framework.<n>Results of a Bayesian ordinal regression model show that self-transcendence values (e.g., benevolence, universalism) were highly prioritized across all models.<n>DeepSeek uniquely downplayed self-enhancement values compared to ChatGPT and Gemini, aligning with collectivist cultural tendencies.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study examines cultural value alignment in large language models (LLMs) by analyzing how Gemini, ChatGPT, and DeepSeek prioritize values from Schwartz's value framework. Using the 40-item Portrait Values Questionnaire, we assessed whether DeepSeek, trained on Chinese-language data, exhibits distinct value preferences compared to Western models. Results of a Bayesian ordinal regression model show that self-transcendence values (e.g., benevolence, universalism) were highly prioritized across all models, reflecting a general LLM tendency to emphasize prosocial values. However, DeepSeek uniquely downplayed self-enhancement values (e.g., power, achievement) compared to ChatGPT and Gemini, aligning with collectivist cultural tendencies. These findings suggest that LLMs reflect culturally situated biases rather than a universal ethical framework. To address value asymmetries in LLMs, we propose multi-perspective reasoning, self-reflective feedback, and dynamic contextualization. This study contributes to discussions on AI fairness, cultural neutrality, and the need for pluralistic AI alignment frameworks that integrate diverse moral perspectives.

Related papers

• Exploring Cultural Variations in Moral Judgments with Large Language Models [0.5356944479760104]
  Using log-probability-based moral justifiability scores, we correlate each model's outputs with survey data covering a broad set of ethical topics.<n>Our results show that many earlier or smaller models often produce near-zero or negative correlations with human judgments.<n> advanced instruction-tuned models (including GPT-4o and GPT-4o-mini) achieve substantially higher positive correlations, suggesting they better reflect real-world moral attitudes.
  arXiv  Detail & Related papers  (2025-06-14T10:16:48Z)
• Beyond Words: Exploring Cultural Value Sensitivity in Multimodal Models [26.051898880298126]
  Investigating value alignment in large language models based on cultural context has become a critical area of research.<n>Similar biases have not been extensively explored in large vision-language models (VLMs)
  arXiv  Detail & Related papers  (2025-02-18T19:03:02Z)
• Value Compass Leaderboard: A Platform for Fundamental and Validated Evaluation of LLMs Values [76.70893269183684]
  Large Language Models (LLMs) achieve remarkable breakthroughs, aligning their values with humans has become imperative.<n>Existing evaluations focus narrowly on safety risks such as bias and toxicity.<n>Existing benchmarks are prone to data contamination.<n>The pluralistic nature of human values across individuals and cultures is largely ignored in measuring LLMs value alignment.
  arXiv  Detail & Related papers  (2025-01-13T05:53:56Z)
• ValuesRAG: Enhancing Cultural Alignment Through Retrieval-Augmented Contextual Learning [1.1343849658875087]
  ValuesRAG is a novel framework that integrates cultural and demographic knowledge dynamically during text generation.<n>We evaluate ValuesRAG using 6 diverse regional datasets and show that it consistently outperforms baselines.<n>Our findings underscore the potential of dynamic retrieval-based methods to bridge the gap between global LLM capabilities and localized cultural values.
  arXiv  Detail & Related papers  (2025-01-02T03:26:13Z)
• LLMs as mirrors of societal moral standards: reflection of cultural divergence and agreement across ethical topics [0.5852077003870417]
  Large language models (LLMs) have become increasingly pivotal in various domains due to the recent advancements in their performance capabilities.<n>This study investigates whether LLMs accurately reflect cross-cultural variations and similarities in moral perspectives.
  arXiv  Detail & Related papers  (2024-12-01T20:39:42Z)
• Large Language Models as Mirrors of Societal Moral Standards [0.5852077003870417]
  Language models can, to a limited extent, represent moral norms in a variety of cultural contexts.<n>This study evaluates the effectiveness of these models using information from two surveys, the WVS and the PEW, that encompass moral perspectives from over 40 countries.<n>The results show that biases exist in both monolingual and multilingual models, and they typically fall short of accurately capturing the moral intricacies of diverse cultures.
  arXiv  Detail & Related papers  (2024-12-01T20:20:35Z)
• CIVICS: Building a Dataset for Examining Culturally-Informed Values in Large Language Models [59.22460740026037]
  "CIVICS: Culturally-Informed & Values-Inclusive Corpus for Societal impacts" dataset is designed to evaluate the social and cultural variation of Large Language Models (LLMs) We create a hand-crafted, multilingual dataset of value-laden prompts which address specific socially sensitive topics, including LGBTQI rights, social welfare, immigration, disability rights, and surrogacy.
  arXiv  Detail & Related papers  (2024-05-22T20:19:10Z)
• Beyond Human Norms: Unveiling Unique Values of Large Language Models through Interdisciplinary Approaches [69.73783026870998]
  This work proposes a novel framework, ValueLex, to reconstruct Large Language Models' unique value system from scratch. Based on Lexical Hypothesis, ValueLex introduces a generative approach to elicit diverse values from 30+ LLMs. We identify three core value dimensions, Competence, Character, and Integrity, each with specific subdimensions, revealing that LLMs possess a structured, albeit non-human, value system.
  arXiv  Detail & Related papers  (2024-04-19T09:44:51Z)
• Not All Countries Celebrate Thanksgiving: On the Cultural Dominance in Large Language Models [89.94270049334479]
  This paper identifies a cultural dominance issue within large language models (LLMs) LLMs often provide inappropriate English-culture-related answers that are not relevant to the expected culture when users ask in non-English languages.
  arXiv  Detail & Related papers  (2023-10-19T05:38:23Z)
• Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and Duties [68.66719970507273]
  Value pluralism is the view that multiple correct values may be held in tension with one another. As statistical learners, AI systems fit to averages by default, washing out potentially irreducible value conflicts. We introduce ValuePrism, a large-scale dataset of 218k values, rights, and duties connected to 31k human-written situations.
  arXiv  Detail & Related papers  (2023-09-02T01:24:59Z)
• Heterogeneous Value Alignment Evaluation for Large Language Models [91.96728871418]
  Large Language Models (LLMs) have made it crucial to align their values with those of humans. We propose a Heterogeneous Value Alignment Evaluation (HVAE) system to assess the success of aligning LLMs with heterogeneous values.
  arXiv  Detail & Related papers  (2023-05-26T02:34:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.