Model Alignment

Model alignment refers to the challenge of training AI systems—particularly large language models—to behave in ways that reflect human values, intentions, and ethical principles. As language models become more capable and are deployed in high-stakes applications, ensuring they act in accordance with human preferences and safety constraints becomes increasingly critical.

The core challenge is one of specification: how do we formally express what we want a language model to do? Unlike traditional software with explicit code, language models operate as learned functions over text. Alignment techniques attempt to shape model behavior through training signals—most commonly through reinforcement learning from human feedback (RLHF)—where human annotators provide guidance on which outputs are more aligned with human values.

Related topics

• AI Safety (broader AI safety research)
• RLHF Training (primary technique for alignment)
• Bias in Language Models (ethical dimension of fairness)