ProSwitch: Knowledge-Guided Instruction Tuning to Switch Between Professional and Non-Professional Responses

ACL ARR 2024 December Submission255 Authors

Abstract: 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.

Paper Type: Long

Research Area: Generation

Research Area Keywords: Generation, Sentiment Analysis, Stylistic Analysis, and Argument Mining, Language Modeling, NLP Applications, Question Answering

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English

Submission Number: 255