Standardize: Aligning Language Models with Expert-Defined Standards for Content Generation
This addresses the need for domain-specific quality control in fields like education, though it is incremental as it builds on existing controllable text generation methods.
The paper tackles the problem of aligning language models with expert-defined standards for content generation, showing that models can achieve a 45% to 100% increase in precise accuracy across various LLMs.
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 a 45% to 100% increase in precise accuracy across open and commercial LLMs evaluated, 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.