Prior work in style-controlled text generation has focused on tasks such as
emulating the style of prolific literary authors, producing formal or informal
text, and the degree of toxicity of generated text. Plentiful demonstrations of
these styles are available, and as a result modern language models are often
able to emulate them, either via prompting or discriminative control. However,
in applications such as writing assistants, it is desirable for language models
to produce text in an author-specific style on the basis of a small writing
sample. We find that instruction-tuned language models can struggle to
reproduce author-specific style demonstrated in a prompt. Instead, we propose
to guide a language model to generate text in a target style using
contrastively-trained representations that capture stylometric features. A
central challenge in doing so is that an author's writing is characterized by
surprising token choices under a generic language model. To reconcile this
tension, we combine generative re-scoring to achieve an author-specific model,
with discriminative control to ensure style consistency at the sequence-level.
The combination of these approaches is found to be particularly effective at
adhering to an author-specific style in a variety of conditions, including
unconditional generation and style transfer, and is applicable to any
underlying language model without requiring fine-tuning.

通过对比训练的表示捕捉风格特征，从而引导语言模型以目标风格生成文本，实现满足作者特定风格的生成，而不需要微调底层语言模型。