ProSwitch: Knowledge-Guided Instruction Tuning to Switch Between Professional and Non-Professional Responses
This addresses a specific need for AI systems to adapt communication styles in professional domains, though it is incremental as it builds on existing instruction tuning methods.
The paper tackles the problem of enabling large language models to switch between professional and non-professional response styles, a capability that is underexplored, and introduces ProSwitch, which outperforms baselines in this task.
Large Language Models (LLMs) have demonstrated efficacy in various linguistic applications, including question answering and controlled text generation. However, studies into their ability to switch between opposite styles of responses in professional domains remain underexplored. This study introduces a novel approach, named ProSwitch, which enables a language model to switch between professional and non-professional answers, by tuning and evaluating through the guidance of domain and style knowledge. ProSwitch unfolds in three phases: LLM-augmented preparation to collect domain knowledge and QA pairs, instruction tuning to optimize LLMs with multiple levels of knowledge, and comprehensive evaluation to assess both style discrimination and reference-based quality of the generated text. Comparative analysis of ProSwitch against general and specialized LLMs reveals that our approach outperforms baselines in switching between professional and non-professional responses.