@inproceedings{zhang-etal-2026-lets,
    title = "Let{'}s Simplify Step by Step: Guiding {LLM} Towards Multilingual Unsupervised Proficiency-Controlled Sentence Simplification",
    author = "Zhang, Jingshen  and
      Qiu, Xin Ying  and
      Lu, Lifang  and
      Huang, Zhuhua  and
      Hu, Yutao  and
      Wu, Yuechang  and
      Lu, JunYu",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.279/",
    pages = "5274--5290",
    ISBN = "979-8-89176-386-9",
    abstract = "Large language models demonstrate limited capability in proficiency-controlled sentence simplification, particularly when simplifying across large readability levels. We propose a framework that decomposes complex simplifications into manageable steps through dynamic path planning, semantic-aware exemplar selection, and chain-of-thought generation with conversation history for coherent reasoning. Evaluation on five languages across two benchmarks shows our approach improves simplification effectiveness while reducing computational steps. Human evaluation confirms the fundamental trade-off between simplification effectiveness and meaning preservation. Notably, even human annotators struggle to agree on semantic preservation judgments, highlighting the inherent complexity of this task. Our work shows that while step-by-step simplification improves control, preserving semantic fidelity during extensive simplification remains an open challenge."
}