Thillainathan, Sarubi; Koller, Alexander
Controllable Text Adaptation Using In-context Learning with Linguistic Features
AAAI2025 AI for Education - Tools, Opportunities, and Risks in the Generative AI Era, 2025.
The diversity in readers’ cognitive abilities, including working memory capacity and prior knowledge, necessitates texts that align with individual comprehension levels. We address the challenge of rewriting text to match readers’ unique needs, approximating readers to specific grade levels. Unlike prior approaches that rely on fine-tuned models and large training datasets, our method leverages in-context learning (ICL), making it effective in data-sparse scenarios. By precisely controlling linguistic features such as syntactic depth, our approach delivers tailored rewrites aligned with specific grade levels. We demonstrate state-of-the-art performance in generating grade-specific adaptations, highlighting the potential of ICL-based methods to enhance text accessibility and inclusivity.