Adapting a large language model for multiple-attribute text style transfer via fine-tuning can be challenging due to the significant amount of computational resources and labeled data required for the specific task. In this paper, we address this challenge by introducing AdapterTST, a framework that freezes the pre-trained model's original parameters and enables the development of a multiple-attribute text style transfer model. Using BART as the backbone model, Adapter-TST utilizes different neural adapters to capture different attribute information, like a plug-in connected to BART. Our method allows control over multiple attributes, like sentiment, tense, voice, etc., and configures the adapters' architecture to generate multiple outputs respected to attributes or compositional editing on the same sentence. We evaluate the proposed model on both traditional sentiment transfer and multiple-attribute transfer tasks. The experiment results demonstrate that Adapter-TST outperforms all the state-of-the-art baselines with significantly lesser computational resources. We have also empirically shown that each adapter is able to capture specific stylistic attributes effectively and can be configured to perform compositional editing.

翻译：微调大型语言模型以实现多属性文本风格转换具有挑战性，因为该特定任务需要大量计算资源和标注数据。本文通过引入Adapter-TST框架应对这一挑战，该框架冻结预训练模型的原始参数，并支持开发多属性文本风格转换模型。以BART作为骨干模型，Adapter-TST利用不同的神经适配器捕获不同属性信息，如同插入BART的可插拔模块。我们的方法能够控制情感、时态、语态等多种属性，并通过配置适配器架构，在相同句子上生成对应单一属性或组合编辑的多个输出。我们在传统情感转换和多属性转换任务上评估了所提模型。实验结果表明，Adapter-TST以显著更少的计算资源超越所有最先进基线方法。我们还通过实验验证了每个适配器能有效捕获特定风格属性，并可配置执行组合编辑。