SynDec: A Synthesize-then-Decode Approach for Arbitrary Textual Style Transfer via Large Language Models
This work addresses the problem of arbitrary textual style transfer for users of large language models, offering an incremental improvement over existing methods.
The paper tackles the challenges of arbitrary textual style transfer with large language models by proposing a Synthesize-then-Decode approach that automatically synthesizes prompts and amplifies their effect during decoding, resulting in outperforming existing methods on five out of six benchmarks with up to a 9% accuracy increase.
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.