  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.
