Standardize: Aligning Language Models with Expert-Defined Standards for
  Content Generation
Joseph Marvin Imperial Gail Forey Harish Tayyar Madabushi
Domain experts across engineering, healthcare, and education follow strict
standards for producing quality content such as technical manuals, medication
instructions, and children's reading materials. However, current works in
controllable text generation have yet to explore using these standards as
references for control. Towards this end, we introduce Standardize, a
retrieval-style in-context learning-based framework to guide large language
models to align with expert-defined standards. Focusing on English language
standards in the education domain as a use case, we consider the Common
European Framework of Reference for Languages (CEFR) and Common Core Standards
(CCS) for the task of open-ended content generation. Our findings show that
models can gain 40% to 100% increase in precise accuracy for Llama2 and GPT-4,
respectively, demonstrating that the use of knowledge artifacts extracted from
standards and integrating them in the generation process can effectively guide
models to produce better standard-aligned content.