矿山大模型行业知识问答能力优化数据集

随着矿山行业数字化转型的加速推进，大模型在知识问答场景中的应用愈发广泛。然而，现有的大模型在矿山行业知识问答方面，存在答案准确性欠佳、知识覆盖不全面等问题，缺少能够有效提升其知识问答能力的有力工具。为此，精心打造了矿山大模型行业知识问答能力优化数据集，旨在通过丰富且专业的矿山知识问答数据，对大模型进行针对性训练，从而显著优化大模型在矿山知识问答领域的表现，提高答案的准确性与全面性。

Against the backdrop of accelerating digital transformation in the mining industry, large language models (LLMs) have witnessed growing adoption in knowledge question-answering (QA) scenarios. However, existing LLMs exhibit shortcomings including unsatisfactory answer accuracy and insufficient knowledge coverage when deployed for mining industry knowledge QA, with no effective tools available to substantially enhance their QA performance. To address these issues, we have meticulously constructed a mining-specific LLM knowledge QA capability optimization dataset. This dataset is designed to conduct targeted training for LLMs using abundant and professional mining knowledge QA data, thereby significantly optimizing the performance of LLMs in the mining knowledge QA domain and improving the accuracy and comprehensiveness of their provided answers.