Lexical Simplification (LS) aims to simplify text at the lexical level.
Existing methods rely heavily on annotated data, making it challenging to apply
in low-resource scenarios. In this paper, we propose a novel LS method without
parallel corpora. This method employs an Adversarial Editing System with
guidance from a confusion loss and an invariance loss to predict lexical edits
in the original sentences. Meanwhile, we introduce an innovative LLM-enhanced
loss to enable the distillation of knowledge from Large Language Models (LLMs)
into a small-size LS system. From that, complex words within sentences are
masked and a Difficulty-aware Filling module is crafted to replace masked
positions with simpler words. At last, extensive experimental results and
analyses on three benchmark LS datasets demonstrate the effectiveness of our
proposed method.

在低资源场景下，我们提出了一种无需平行语料库的新型词汇简化方法，该方法利用对原始句子进行混淆损失和不变性损失的指导来预测词汇编辑，并引入了创新的 LLM 增强损失，将大型语言模型的知识蒸馏到小型词汇简化系统中。大量的实验结果和三个基准 LS 数据集的分析证明了我们提出方法的有效性。