ProSwitch：用于在专业与非专业回复间切换的知识引导指令微调
ProSwitch: Knowledge-Guided Instruction Tuning to Switch Between Professional and Non-Professional Responses
AI总结：
针对大语言模型在专业领域切换回复风格的研究不足问题，提出名为ProSwitch的知识引导指令微调方法，通过三阶段流程实现专业与非专业回复切换，效果优于通用及专用基线大模型。
摘要：
大语言模型（LLM）已在问答、受控文本生成等各类语言应用中展现出有效性，但针对其在专业领域中切换对立风格回复能力的研究仍有待深入。本研究提出一种名为ProSwitch的新方法，通过领域与风格知识的引导进行微调与评估，使语言模型能够在专业与非专业答案间切换。ProSwitch分为三个阶段：LLM增强准备阶段以收集领域知识与问答对，指令微调阶段利用多层级知识优化LLM，以及综合评估阶段以评估生成文本的风格区分度与基于参考的质量。将ProSwitch与通用及专用LLM进行对比分析表明，本方法在专业与非专业回复切换任务上优于基线模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
