用自动构造提示+强化解码，让大模型更自由地实现文本风格迁移。
SynDec: A Synthesize-then-Decode Approach for Arbitrary Textual Style Transfer via Large Language Models
- 先自动选样分析风格，再生成高质量提示
- 在解码时增强风格差异，提升迁移准确率9%
- 适合需要灵活风格转换的研究者和开发者
大语言模型在文本风格迁移中日益重要，但任意风格迁移面临两大挑战：依赖人工设计提示、模型固有风格偏见。本文提出合成-解码（SynDec）新方法，自动生成高质量提示并增强其在解码中的作用。具体而言，通过选取代表性少样本、进行四维风格分析并重排序候选提示，实现提示合成；在解码阶段，通过最大化有无提示及正负样本间输出概率的对比度，放大风格转移效果。大量实验表明，SynDec在六个基准中的五个上超越现有最优方法（如现代英语转伊丽莎白时代英语准确率提升达9%）。消融实验进一步验证了该方法的有效性。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are emerging as dominant forces for textual style transfer. However, for arbitrary style transfer, LLMs face two key challenges: (1) considerable reliance on manually-constructed prompts and (2) rigid stylistic biases inherent in LLMs. In this paper, we propose a novel Synthesize-then-Decode (SynDec) approach, which automatically synthesizes high-quality prompts and amplifies their roles during decoding process. Specifically, our approach synthesizes prompts by selecting representative few-shot samples, conducting a four-dimensional style analysis, and reranking the candidates. At LLM decoding stage, the TST effect is amplified by maximizing the contrast in output probabilities between scenarios with and without the synthesized prompt, as well as between prompts and negative samples. We conduct extensive experiments and the results show that SynDec outperforms existing state-of-the-art LLM-based methods on five out of six benchmarks (e.g., achieving up to a 9\% increase in accuracy for modern-to-Elizabethan English transfer). Detailed ablation studies further validate the effectiveness of SynDec.
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