为采矿业定制的70亿参数大模型，知识测试表现提升14%。
MiningGPT -- A Domain-Specific Large Language Model for the Mining Industry
- 基于Mistral 7B微调，专精采矿领域指令理解。
- 在专业测试中比原模型高14%准确率。
- 适合矿业研究与工程人员快速获取专业知识。
生成式大语言模型虽具备类人语言能力，但在特定领域理解上仍有不足。为此，研究社区正致力于构建多个领域的专用大模型。本文聚焦采矿行业，该行业对全球经济贡献巨大。我们提出MiningGPT，一个70亿参数的采矿领域专用指令跟随型大模型。该模型在采矿领域知识测试中相较其基础模型Mistral 7B instruct提升了14%的得分，展现出更强的领域适应性与知识掌握能力。
原文摘要 · Abstract (English)
Recent advancements of generative LLMs (Large Language Models) have exhibited human-like language capabilities but have shown a lack of domain-specific understanding. Therefore, the research community has started the development of domain-specific LLMs for many domains. In this work we focus on discussing how to build mining domain-specific LLMs, as the global mining industry contributes significantly to the worldwide economy. We report on MiningGPT, a mining domain-specific instruction-following 7B parameter LLM model which showed a 14\% higher mining domain knowledge test score as compared to its parent model Mistral 7B instruct.
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