ProSwitch: Knowledge-Guided Instruction Tuning to Switch Between Professional and Non-Professional Responses
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916519022166016 |
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| author | Zong, Chang Chen, Yuyan Lu, Weiming Shao, Jian Huang, Yongfeng Chang, Heng Zhuang, Yueting |
| author_facet | Zong, Chang Chen, Yuyan Lu, Weiming Shao, Jian Huang, Yongfeng Chang, Heng Zhuang, Yueting |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_09131 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ProSwitch: Knowledge-Guided Instruction Tuning to Switch Between Professional and Non-Professional Responses Zong, Chang Chen, Yuyan Lu, Weiming Shao, Jian Huang, Yongfeng Chang, Heng Zhuang, Yueting Computation and Language Artificial Intelligence 68T50 I.2.7 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. |
| title | ProSwitch: Knowledge-Guided Instruction Tuning to Switch Between Professional and Non-Professional Responses |
| topic | Computation and Language Artificial Intelligence 68T50 I.2.7 |
| url | https://arxiv.org/abs/2403.09131 |