Readability-controlled text simplification (RCTS) rewrites texts to lower readability levels while preserving their meaning. RCTS models often depend on parallel corpora with readability annotations on both source and target sides. Such datasets are scarce and difficult to curate, especially at the sentence level. To reduce reliance on parallel data, we explore using instruction-tuned large language models for zero-shot RCTS. Through automatic and manual evaluations, we examine: (1) how different types of contextual information affect a model's ability to generate sentences with the desired readability, and (2) the trade-off between achieving target readability and preserving meaning. Results show that all tested models struggle to simplify sentences (especially to the lowest levels) due to models' limitations and characteristics of the source sentences that impede adequate rewriting. Our experiments also highlight the need for better automatic evaluation metrics tailored to RCTS, as standard ones often misinterpret common simplification operations, and inaccurately assess readability and meaning preservation.

本研究针对可读性控制文本简化（RCTS）中的数据不足问题，提出使用指令调优的大语言模型进行零-shot RCTS。研究发现，模型在简化句子时面临挑战，尤其是在实现最低可读性水平与保持原意之间存在权衡，同时强调了需要改进自动评估指标以更准确地评估简化操作。

分析零-shot可读性控制的句子简化