用大模型零样本实现可控可读性文本简化，发现效果受限于模型与原文特性。
Analysing Zero-Shot Readability-Controlled Sentence Simplification
- 使用指令微调的大模型进行零样本可读性控制简化
- 模型在降低可读性至最低水平时表现差，且难保语义完整
- 现有评估指标不准确，需专用于文本简化的评测方法
可读性控制的文本简化（RCTS）旨在改写文本以降低可读性等级，同时保持原意。现有RCTS模型通常依赖双语平行语料库，其中源句和目标句均有可读性标注，但这类数据稀缺且难以在句子级别构建。为减少对平行数据的依赖，我们探索使用指令微调的大语言模型实现零样本RCTS。通过自动与人工评估，考察了：(1) 不同上下文信息如何影响模型生成目标可读性句子的能力；(2) 达到目标可读性与保持语义之间的权衡。结果表明，所有测试模型在简化句子（尤其是降至最低可读性级别）时表现不佳，原因在于模型局限性和源句本身的特征阻碍充分重写。实验还揭示现有自动评估指标存在缺陷，标准指标常误判常见简化操作，导致可读性与语义保留评估失真，亟需针对RCTS设计更精准的评估工具。
原文摘要 · Abstract (English)
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.
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