Summary of Proswitch: Knowledge-guided Instruction Tuning to Switch Between Professional and Non-professional Responses, by Chang Zong et al.
ProSwitch: Knowledge-Guided Instruction Tuning to Switch Between Professional and Non-Professional Responses
by Chang Zong, Yuyan Chen, Weiming Lu, Jian Shao, Yongfeng Huang, Heng Chang, Yueting Zhuang
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper introduces a novel approach called ProSwitch, which enables Large Language Models (LLMs) to switch between professional and non-professional answers by tuning and evaluating through the guidance of domain and style knowledge. This three-phase approach includes 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. The results show that ProSwitch outperforms general and specialized LLMs in switching between professional and non-professional responses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ProSwitch is a new way for language models to give different answers depending on whether they’re talking about work or something else. It helps the model learn from experts in a specific field, and then it can answer questions like an expert would. The model gets better at this by going through three steps: learning, tuning, and testing. When compared to other models that only know general things or are specialized in one area, ProSwitch does much better at giving different answers for professional and non-professional topics. |
Keywords
» Artificial intelligence » Instruction tuning
