Framework Enables Controlled Sentence Simplification With LLMs

A Feb. 7, 2026 arXiv preprint presents a framework that decomposes proficiency-controlled sentence simplification into dynamic path planning, semantic-aware exemplar selection, and chain-of-thought generation with conversation history. Evaluated on five languages across two benchmarks, the approach improves simplification effectiveness while reducing computational steps by 22–42%. Human evaluation reveals a trade-off between simplification and meaning preservation, and annotator disagreement highlights evaluation challenges.