Distilling Text Style Transfer With Self-Explanation From LLMs
<|reference_start|>Distilling Text Style Transfer With Self-Explanation From LLMs: 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.<|reference_end|>
@article{zhang2024distilling,
    title={Distilling Text Style Transfer With Self-Explanation From LLMs},
    author={Chiyu Zhang, Honglong Cai, Yuezhang (Music) Li, Yuexin Wu, Le Hou,
  Muhammad Abdul-Mageed},
    journal={arXiv preprint arXiv:2403.01106},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.01106},
    primaryClass={cs.CL cs.AI}
}