Toward Safer Mining: MS-GLM for Coal Mine Safety via Domain-Adaptive Pretraining and Multi-Task Instruction Tuning

Abstract Ensuring safety in coal mining operations requires an accurate understanding and execution of complex regulatory and procedural knowledge. However, existing general-purpose language models often lack domain-specific adaptation, limiting their effectiveness in high-risk industrial scenarios. This study presents MS-GLM, a domain-adaptive language model tailored for coal mine safety applications. A multisource and multitask dataset, Mine Safety Intelligence (MSI), was constructed, comprising of 10GB plain text and 10,000 high-quality instances that cover safety protocols, emergency response, and hazard prevention. The data set integrates human-annotated content and filtered ChatGPT output, ensuring both diversity and factual accuracy.MS-GLM is trained using a two-stage framework that combines domain-adaptive pretraining and LoRA-based supervised fine-tuning. Experimental evaluations on a custom coal mine QA benchmark (CMS-QED) and general-purpose NLP tasks demonstrate that MS-GLM significantly improves factual accuracy and safety-related reasoning while maintaining competitive inference efficiency. Ablation studies confirm that pretraining and fine-tuning contribute complementary gains in both precision and generalization.These results suggest that domain-specific language modeling strategies can effectively enhance the decision-support capabilities of expert systems in safety-critical industries.