Reinforcement Learning from Human Feedback: Whose Culture, Whose Values, Whose Perspectives?

2024-07-02

arXiv.org

AI Summary

This study addresses implicit cultural biases and value monism in Reinforcement Learning from Human Feedback (RLHF) for large language models (LLMs). Methodologically, it pioneers the systematic application of social epistemology to deconstruct RLHF’s value-embedding mechanisms, proposing a cross-cultural, multi-perspective framework for integrating human feedback. It combines philosophical critique, normative modeling, and governance-oriented empirical research to develop an actionable governance roadmap. Key contributions include: (1) establishing a feedbacker diversity assurance mechanism; (2) advancing explicit, negotiable modeling of values in LLM alignment; and (3) developing a cross-cultural alignment evaluation framework. Collectively, these advances enhance LLMs’ human responsiveness and ethical inclusivity, offering both theoretical grounding and practical tools for value alignment in globally diverse AI contexts.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Morality & Value-based AI
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

We argue for the epistemic and ethical advantages of pluralism in Reinforcement Learning from Human Feedback (RLHF) in the context of Large Language Models (LLM). Drawing on social epistemology and pluralist philosophy of science, we suggest ways in which RHLF can be made more responsive to human needs and how we can address challenges along the way. The paper concludes with an agenda for change, i.e. concrete, actionable steps to improve LLM development.

Problem

• Cultural Diversity
• Ethical Norms
• Human Feedback Reinforcement Learning

Innovation

• Human Feedback Integration
• Social Knowledge Incorporation
• Multi-perspective Science in Reinforcement Learning