Style is used to convey authors' intentions and attitudes. Despite the
success of large pre-trained language models on style classification, prior
work relies on fine-tuning with labeled examples. Prompting large language
models to classify style without fine-tuning is challenging because language
styles can be difficult to define. In this study, we investigate the
effectiveness of style lexicons as a means for instructing language models how
to identify new styles that are unseen during training. Our experiments show
that lexicon-based instructions improve transfer zero-shot performance
significantly. We will release our code and data.

本研究探讨了基于词典的指示如何发现训练时未曾涉及的新风格，以指导大型预先训练的语言模型进行文本风格分类，并展示了这种方法对提高迁移零样本性能的显著效果。

使用词汇表进行指令调整的零样式分类

Instruction Tuning with Lexicons for Zero-Shot Style Classification

Prompting approaches have been recently explored in text style transfer,
where a textual prompt is used to query a pretrained language model to generate
style-transferred texts word by word in an autoregressive manner. However, such
a generation process is less controllable and early prediction errors may
affect future word predictions. In this paper, we present a prompt-based
editing approach for text style transfer. Specifically, we prompt a pretrained
language model for style classification and use the classification probability
to compute a style score. Then, we perform discrete search with word-level
editing to maximize a comprehensive scoring function for the style-transfer
task. In this way, we transform a prompt-based generation problem into a
classification one, which is a training-free process and more controllable than
the autoregressive generation of sentences. In our experiments, we performed
both automatic and human evaluation on three style-transfer benchmark datasets,
and show that our approach largely outperforms the state-of-the-art systems
that have 20 times more parameters. Additional empirical analyses further
demonstrate the effectiveness of our approach.

该论文提出了一种基于提示的文本风格转换编辑方法，其中使用预训练语言模型进行风格分类，使用分类概率计算风格得分，然后通过词级别的编辑，以最大化全面的评分函数来实现风格转换任务，实验表明其性能远高于具有 20 倍参数的现有技术。