specification-grounded

SpecAlign Framework Uses Synthetic Data to Align Large Language Models with Specific Policies

A research paper introduces SpecAlign, a framework that generates synthetic training data from provider-authored model specifications to align large language models with specific policies. The method combines structured rule annotation, controllable instantiation, and multi-agent adversarial data synthesis to create preference pairs for fine-tuning. Experiments show improved rule compliance without sacrificing general capabilities.