  Text Style Transfer (TST) seeks to alter the style of text while retaining
its core content. Given the constraints of limited parallel datasets for TST,
we propose CoTeX, a framework that leverages large language models (LLMs)
alongside chain-of-thought (CoT) prompting to facilitate TST. CoTeX distills
the complex rewriting and reasoning capabilities of LLMs into more streamlined
models capable of working with both non-parallel and parallel data. Through
experimentation across four TST datasets, CoTeX is shown to surpass traditional
supervised fine-tuning and knowledge distillation methods, particularly in
low-resource settings. We conduct a comprehensive evaluation, comparing CoTeX
against current unsupervised, supervised, in-context learning (ICL) techniques,
and instruction-tuned LLMs. Furthermore, CoTeX distinguishes itself by offering
transparent explanations for its style transfer process.
