  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.
