@inproceedings{zhang-etal-2025-decoupled,
    title = "A Decoupled Multi-Agent Framework for Complex Text Style Transfer",
    author = "Zhang, Lingxi  and
      Chuang, Yu-Neng  and
      Wang, Guanchu  and
      Tang, Ruixiang  and
      Cai, Xuanting  and
      Shenoy, Rajesh  and
      Hu, Xia",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1166/",
    doi = "10.18653/v1/2025.findings-emnlp.1166",
    pages = "21393--21403",
    ISBN = "979-8-89176-335-7",
    abstract = "Text style transfer (TST) modifies a source sentence to match a target style while preserving its semantics. While existing models perform well on simple styles like sentiment and formality, they struggle with complex, entangled styles such as poetry and brand-specific tones, which require advanced operations to disentangle content and style. We propose a multi-agent self-check framework that contains a large language model (LLM) as a planner for disentangling subtasks and expert agents for executing the subtasks. This training-free multi-agent framework decomposes TST into manageable components, enabling iterative refinement through a self-check module that balances style adherence and content preservation. Experiments on both simple and complex style datasets show our framework significantly improves style strength and content preservation, with strong adaptability in few-shot settings."
}