Empower Large Language Model To Perform Better On Industrial Domain-specific Question Answering

Yang Fangkai, Zhao Pu, Wang Zezhong, Wang Lu, Zhang Jue, Garg Mohit, Lin Qingwei, Rajmohan Saravan, Zhang Dongmei. Arxiv 2023

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Applications Attention Mechanism Has Code Model Architecture RAG

Large Language Model (LLM) has gained popularity and achieved remarkable results in open-domain tasks, but its performance in real industrial domain-specific scenarios is average due to its lack of specific domain knowledge. This issue has attracted widespread attention, but there are few relevant benchmarks available. In this paper, we provide a benchmark Question Answering (QA) dataset named MSQA, centered around Microsoft products and IT technical problems encountered by customers. This dataset contains industry cloud-specific QA knowledge, an area not extensively covered in general LLMs, making it well-suited for evaluating methods aiming to enhance LLMs’ domain-specific capabilities. In addition, we propose a new model interaction paradigm that can empower LLM to achieve better performance on domain-specific tasks where it is not proficient. Extensive experiments demonstrate that the approach following our method outperforms the commonly used LLM with retrieval methods. We make our source code and sample data available at: https://aka.ms/Microsoft_QA.

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