SpecAlign: Efficient Specification-Grounded Alignment of Large Language Models via Synthetic Data

The paper introduces SpecAlign, a framework designed for specification-grounded alignment of large language models (LLMs), which synthesizes alignment data directly from provider-authored model specifications. By utilizing structured rule annotation and multi-agent adversarial data synthesis, SpecAlign generates fine-grained preference pairs that enhance compliance with specific guidelines while maintaining general capabilities. This approach allows for more effective and scalable adaptation of LLMs to evolving policy requirements, addressing the limitations of traditional alignment methods.