EthosGPT: Charting the Human Values Landscape on a Global Scale
Project Overview
EthosGPT is an innovative project aimed at mapping and understanding human values on a global scale. By leveraging advanced language models and ethical frameworks, it seeks to provide insights into the diverse moral landscapes across cultures and societies.
Interactive Tools
Development Resources
📚 Table of Contents
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📖 Project Overview
Introduction to EthosGPT and its mission
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✨ Key Features and Core Components
Exploring the main elements of the system
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🌟 Why EthosGPT?
Understanding the unique value proposition
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🔍 How It Works
Detailed explanation of the technology
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📚 References
Sources and further reading
Project Overview
Background and Motivation
What is EthosGPT?
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]
Why Value Diversity Matters
"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.
The Vision of EthosGPT
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.
Our Commitment to Diversity and Inclusion
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.
Key Features and Core Components
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Cultural Values Analysis
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.
Survival vs Self-Expression Values: A scale reflecting the shift from survival priorities to self-expression and quality-of-life concerns.
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Regional Value Comparison
Data normalized into z-scores for 107 countries/territories, grouped into 8 cultural regions:
Regions include: Confucian, Protestant Europe, Latin America, African-Islamic, etc.
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Key Regional Insights
The Confucian region exhibits the highest discrepancies in both indices.
Protestant Europe and Latin America exceed benchmarks for alignment differences.
Protestant Europe and Latin America exceed benchmarks for alignment differences.
Prompt-Based Evaluation
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Assessing LLMs with Structured Prompts
Assess LLMs using structured prompts simulating responses of an "average individual" from specific countries or regions.
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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).
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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.
Interactive Data Tools
Advanced Analysis Tools
Powerful tools for analyzing LLM outputs that enable meaningful cross-domain collaboration and research
Cultural Diversity Explorer
Interactive visualizations for exploring cultural diversity patterns and alignment metrics across regions
Open Source Visualizations
Comprehensive visualization suite for examining biases and alignment through open-source tools
Interactive Visualization Demo 1
Interactive Visualization Demo 2.1
Interactive Visualization Demo 2.2
Interactive Visualization Demo 3
Interactive Data Tools
🌍 Cultural Values Comparison
Compare cultural value indices derived from human survey data with ChatGPT-generated responses. Examine ChatGPT's alignment with cultural dimensions like individualism and power distance, and identify biases in AI outputs compared to human data.
💻 GitHub Repo
📊 Mean Square Error (MSE) Analysis
Analyze the accuracy of ChatGPT's cultural value predictions using MSE metrics. Assess regional accuracy, identify areas for improvement, and compare ChatGPT's cultural representations across regions.
💻 GitHub Repo
🗺️ Cultural Values Map
Explore cultural value indices on an interactive global map. Gain a visual understanding of global cultural indices and compare ChatGPT's outputs with survey data across nations.
💻 GitHub Repo
Why EthosGPT?
LLMs often risk homogenizing values, reflecting dominant cultural biases and marginalizing underrepresented perspectives.
Preserving Cultural Diversity
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.
Advancing Ethical AI Alignment
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.
Open-Source and Research-Driven
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.
How It Works
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1. Prompt Input
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2. Response Evaluation
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3. Visualization
1. Prompt Input Carefully crafted prompts probe LLM responses across cultural and ethical contexts.
2. Response Evaluation Alignment is measured using frameworks like Hofstede's cultural dimensions.
3. Visualization Results are displayed through intuitive visualizations to highlight strengths and biases.
References
- 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.