Large Language Models (LLMs) have demonstrated efficacy in various linguistic
applications, including text summarization and controlled text generation.
However, studies into their capacity of switching between styles via
fine-tuning remain underexplored. This study concentrates on textual
professionalism and introduces a novel methodology, named ProSwitch, which
equips a language model with the ability to produce both professional and
non-professional responses through knowledge-guided instruction tuning.
ProSwitch unfolds across three phases: data preparation for gathering domain
knowledge and training corpus; instruction tuning for optimizing language
models with multiple levels of instruction formats; and comprehensive
evaluation for assessing the professionalism discrimination and reference-based
quality of generated text. Comparative analysis of ProSwitch against both
general and specialized language models reveals that our approach outperforms
baselines in switching between professional and non-professional text
generation.

本研究介绍了一种名为 ProSwitch 的新型方法，通过知识引导的指导调优，使语言模型能够在专业和非专业响应之间切换，从而提供了一种解决文本专业性问题的能力。与通用和专用语言模型的比较分析显示，我们的方法在专业和非专业文本生成之间的切换方面优于基准模型。