Large language models (LLMs) are transforming global decision-making and societal systems. Their ability to process diverse data and align with human values is both a remarkable strength and a critical risk. While LLMs excel at navigating cultural, economic, and political differences, they also risk homogenizing values—a process akin to the loss of biodiversity threatening ecological resilience. [3] [4]
“Diversity is the foundation of innovation, adaptability, and resilience.”
– UNESCO
Just as ecosystems thrive on biodiversity, societies prosper through the rich interplay of varied human value systems. Without this diversity:
- 🛑 Risks: Homogenization could lead to ethical oversights and stagnation in AI-driven decision-making.
- 💡 Opportunities: Preserving cultural values ensures sustainable progress, fostering ethical and inclusive AI innovation.
EthosGPT introduces an open-source framework designed to map and visualize LLMs’ positioning within a multidimensional landscape of human values. Using prompt-based evaluation, EthosGPT examines how effectively AI systems navigate complex global differences in human values.
- 📈 Strengths: Insights into LLMs’ cultural adaptability.
- 🔍 Limitations: Identification of ethical dilemmas where LLMs struggle with nuanced, context-specific scenarios.
EthosGPT bridges disciplines by offering open-source data, code, and interactive tools, inviting global audiences to enhance and engage with its findings.
At EthosGPT, we are dedicated to including as many human cultural heritages as possible in our open-source framework. Our goal is to support the sustainable development of humanity, ensuring AI systems are inclusive, representative, and ethically aligned.
Below is a sketch of flags from 107 countries, grouped by 8 cultural regions, reflecting the diversity of nations covered in EthosGPT.
| African-Islamic | Confucian | Latin America | Catholic Europe | English-Speaking | Orthodox Europe | Protestant Europe | West & South Asia |
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- Visualize LLM performance across cultural and ethical dimensions using comparative analyses of survey data and ChatGPT outputs. [5] [6].
Example 1: Analyze cultural values through indices
- Traditional vs Secular-Rational Values: A scale measuring the emphasis on tradition and authority versus secular and rational perspectives.
- Survival vs Self-Expression Values: A scale reflecting the shift from survival priorities to self-expression and quality-of-life concerns.
Example 2: Explore region-based discrepancies
- Data normalized into z-scores for 107 countries/territories, grouped into 8 cultural regions:
- Regions include: Confucian, Protestant Europe, Latin America, African-Islamic, etc.
- Insights:
- The Confucian region exhibits the highest discrepancies in both indices.
- Protestant Europe and Latin America exceed benchmarks for alignment differences.
- Assess LLMs using structured prompts simulating responses of an "average individual" from specific countries or regions.
Example 1: Comparison with survey data
- Compare ChatGPT's simulated cultural indices against original survey data (Haerpfer et al., 2022).
- Strength: Consistent alignment in secular-rational values for English-Speaking regions (e.g., USA, UK).
- Weakness: Underrepresentation of self-expression values in African-Islamic regions (e.g., Egypt, Morocco).
Example 2: Evaluate discrepancies using MSE analysis
- Mean Square Error (MSE) identifies regions with significant deviations.
- Benchmarks:
- Traditional vs Secular: ~0.4
- Survival vs Self-Expression: ~0.6
- Insights:
- Regions with higher MSE (e.g., Confucian regions) indicate larger deviations between ChatGPT predictions and survey data.
- Analyze LLM outputs with advanced tools that foster cross-domain collaboration.
- Explore cultural diversity, alignment metrics, and biases via open-source visualizations.
Interactive Visualization Demo 1 |
Interactive Visualization Demo 2.1 |
Interactive Visualization Demo 2.2 |
Interactive Visualization Demo 3 |
| 🖼️ Visualization | 📋 Description | 📘 Learning Opportunities | 🌐 Webpage | 💻 Source Code |
|---|---|---|---|---|
| 🌍 Cultural Values Comparison: Survey vs ChatGPT | Compare cultural value indices derived from human survey data with ChatGPT-generated responses. |
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🌐 Open App | 💻 GitHub Repo |
| 📊 Mean Square Error (MSE) Analysis by Region | Analyze the accuracy of ChatGPT’s cultural value predictions using MSE metrics. |
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🌐 Open App | 💻 GitHub Repo |
| 🗺️ Cultural Values Map | Explore cultural value indices on an interactive global map. |
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🌐 Open App | 💻 GitHub Repo |
LLMs often risk homogenizing values, reflecting dominant cultural biases and marginalizing underrepresented perspectives.
| 🌟 | Highlight Diversity: EthosGPT emphasizes the preservation of cultural diversity, enabling AI systems to adapt to and celebrate the rich tapestry of global values. |
| 🔓 | Open-Source Contribution: By offering an open-source framework, EthosGPT invites global contributions to ensure cultural inclusivity and representation. |
Provides actionable insights for developing AI systems that are socially and ethically aligned, ensuring context-aware decision-making.
| ✅ | Context-Aware Decision-Making: Addresses nuanced ethical dilemmas faced by AI in diverse cultural contexts. |
| 📊 | Bias Mitigation: Leverages interactive tools and visualizations to identify and reduce biases in AI systems. |
Built on a research-backed foundation, EthosGPT combines open-source tools and rigorous cultural analysis to drive innovation and inclusivity.
| 📚 | Research-Backed: Studies like CVALUES and CultureLLM provide robust foundations for culturally sensitive AI analysis. |
| 🌐 | Collaboration: EthosGPT offers open-source data, code, and tools, empowering researchers, developers, and policymakers worldwide. |
| 🔍 | Cross-Disciplinary Exploration: Breaks traditional boundaries between AI, ethics, and cultural studies for innovative solutions. |
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Prompt Input
Carefully crafted prompts probe LLM responses across cultural and ethical contexts. -
Response Evaluation
Alignment is measured using frameworks like Hofstede’s cultural dimensions. -
Visualization
Results are displayed through intuitive visualizations to highlight strengths and biases.
- Xu, G., Liu, J., Yan, M., et al. (2023). CVALUES: Measuring the Values of Chinese Large Language Models from Safety to Responsibility. arXiv:2307.09705v1.
- Li, C., Chen, M., Wang, J., et al. (2024). CultureLLM: Incorporating Cultural Differences into Large Language Models. arXiv:2402.10946v2.
- Kharchenko, J., Roosta, T., Chadha, A., & Shah, C. (2024). How Well Do LLMs Represent Values Across Cultures? arXiv:2406.14805v1.
- Tao, Y., Viberg, O., Baker, R. S., & Kizilcec, R. F. (2024). Cultural Bias and Cultural Alignment of Large Language Models. DOI:10.1093/pnasnexus/pgae346.
- Haerpfer, C., Inglehart, R., Moreno, A., Welzel, C., Kizilova, K., Diez-Medrano J., M. Lagos, P. Norris, E. Ponarin & B. Puranen (eds.). (2022). World Values Survey: Round Seven - Country-Pooled Datafile Version 5.0. Madrid, Spain & Vienna, Austria: JD Systems Institute & WVSA Secretariat. DOI:10.14281/18241.24.
- Inglehart, R., Welzel, C. (2005). Modernization, cultural change, and democracy: the human development sequence. Vol. 333. Cambridge University Press.