Diverse Human Value Alignment for Large Language Models via Ethical Reasoning

2025-10-31

AI Summary

Current large language models (LLMs) face two core challenges in value alignment: (1) difficulty in deeply modeling the plurality, context-dependency, and dynamic evolution of human values, and (2) a tendency toward superficial compliance rather than genuine ethical reasoning. To address these, we propose the first ethically grounded, five-step structured reasoning framework—inspired by normative ethical decision theory—comprising fact collection, norm identification, option generation, multi-perspective impact analysis, and reflective evaluation. Implemented via prompt engineering and supervised fine-tuning, the framework enables interpretable and traceable cross-cultural ethical reasoning. We systematically evaluate regional value alignment capability on the SafeWorld benchmark. Experiments demonstrate significant improvements: +18.3% accuracy in social norm identification and enhanced cultural adaptability, alongside superior generalization and robustness in complex ethical scenarios. Our approach establishes a novel paradigm for safe, trustworthy LLM deployment in globally diverse value environments.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Humans and AI: Learning Human Values and Preferences

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Search and Retrieval-Augmented AI: Large language models for search
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

Ensuring that Large Language Models (LLMs) align with the diverse and evolving human values across different regions and cultures remains a critical challenge in AI ethics. Current alignment approaches often yield superficial conformity rather than genuine ethical understanding, failing to address the complex, context-dependent nature of human values. In this paper, we propose a novel ethical reasoning paradigm for LLMs inspired by well-established ethical decision-making models, aiming at enhancing diverse human value alignment through deliberative ethical reasoning. Our framework consists of a structured five-step process, including contextual fact gathering, hierarchical social norm identification, option generation, multiple-lens ethical impact analysis, and reflection. This theory-grounded approach guides LLMs through an interpretable reasoning process that enhances their ability to understand regional specificities and perform nuanced ethical analysis, which can be implemented with either prompt engineering or supervised fine-tuning methods. We perform evaluations on the SafeWorld benchmark that specially designed for regional value alignment. Experimental results demonstrate our framework significantly improves LLM alignment with diverse human values compared to baseline methods, enabling more accurate social norm identification and more culturally appropriate reasoning. Our work provides a concrete pathway toward developing LLMs that align more effectively with the multifaceted values of global societies through interdisciplinary research.

Problem

• Aligning LLMs with diverse global human values
• Addressing superficial conformity in current ethical alignment
• Enhancing cultural context understanding through ethical reasoning

Innovation

• Structured five-step ethical reasoning process
• Contextual fact gathering and norm identification
• Multiple-lens ethical impact analysis framework