用轮转翻译生成风格数据，让大模型高效实现文本风格迁移。
Text Style Transfer with Parameter-efficient LLM Finetuning and Round-trip Translation
- 通过轮转翻译构建无风格文本对，解决风格数据稀缺问题。
- 在四个领域均优于零样本和少样本方法，提升风格准确率与BLEU分数。
- 结合检索增强生成，提升术语与人名一致性，适合实际应用。
本文提出一种基于参数高效微调的大语言模型文本风格迁移新方法。针对风格间平行语料稀缺的问题，研究利用轮转翻译从单语语料中合成平行数据集，生成去除风格特征的‘中性化’文本，使训练与推理阶段具有统一输入风格。实验表明，该方法在四个研究领域上均显著优于零样本提示与少样本上下文学习，各项指标（如BLEU与风格准确率）表现更优。此外，引入检索增强生成（RAG）以获取术语与名称知识，进一步提升了生成结果的鲁棒性与风格一致性。
原文摘要 · Abstract (English)
This paper proposes a novel method for Text Style Transfer (TST) based on parameter-efficient fine-tuning of Large Language Models (LLMs). Addressing the scarcity of parallel corpora that map between styles, the study employs roundtrip translation to synthesize such parallel datasets from monolingual corpora. This approach creates 'neutralized' text devoid of stylistic attributes, essentially creating a shared input style at training-time and inference-time. Experimental results demonstrate consistent superiority of this method over zero-shot prompting and fewshot ICL techniques measured by BLEU scores and style accuracy scores across four investigated domains. Furthermore, the integration of retrieval-augmented generation (RAG) for terminology and name knowledge enhances robustness and stylistic consistency.
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