Scopes of Alignment

2025-01-15

AI Summary

Current large language model alignment research overemphasizes static, universal values—helpfulness, harmlessness, and honesty—while neglecting competence adaptation, response timeliness, and audience heterogeneity. Method: We propose the first formally defined three-dimensional alignment framework—Competence, Transience, and Audience—grounded in conceptual modeling and context-sensitive analysis to establish a scalable, scenario-driven theory of dynamic alignment. We systematically map mainstream techniques—including RLHF, Constitutional AI, and context distillation—onto this 3D space to identify coverage gaps. Contribution/Results: The framework yields a precise, application-oriented alignment taxonomy, enabling fine-grained alignment design per use case. It shifts the paradigm from static value consistency toward dynamic functional applicability, advancing both theoretical foundations and practical deployment of aligned AI systems.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Learning Human Values and Preferences

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Much of the research focus on AI alignment seeks to align large language models and other foundation models to the context-less and generic values of helpfulness, harmlessness, and honesty. Frontier model providers also strive to align their models with these values. In this paper, we motivate why we need to move beyond such a limited conception and propose three dimensions for doing so. The first scope of alignment is competence: knowledge, skills, or behaviors the model must possess to be useful for its intended purpose. The second scope of alignment is transience: either semantic or episodic depending on the context of use. The third scope of alignment is audience: either mass, public, small-group, or dyadic. At the end of the paper, we use the proposed framework to position some technologies and workflows that go beyond prevailing notions of alignment.

Problem

• Language Models
• Ethical AI
• User-Centric Design

Innovation

• Advanced Language Model Capabilities
• Adaptive Response Transience
• Audience-Tailored Reactions