ProSwitch: Knowledge-Guided Instruction Tuning to Generate Professional and Non-Professional Styled Text
<|reference_start|>ProSwitch: Knowledge-Guided Instruction Tuning to Generate Professional and Non-Professional Styled Text: 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.<|reference_end|>
@article{zong2024proswitch:,
    title={ProSwitch: Knowledge-Guided Instruction Tuning to Switch Between
  Professional and Non-Professional Answers},
    author={Chang Zong, Yuyan Chen, Weiming Lu, Jian Shao, Yueting Zhuang},
    journal={arXiv preprint arXiv:2403.09131},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.09131},
    primaryClass={cs.CL cs.AI}
}