Automated Safety Precaution Generation in High-Risk Industries: A Parameter-Efficient Fine-Tuning Approach with Mistral-7B

The mining industry faces complex operational hazards that necessitate systematic risk assessments to enable proactive accident prevention. While Large Language Models (LLMs) offer significant potential for the automated generation of safety measures, the limited availability of domain-specific terminology and high-quality labelled safety data (in low-resource environments) hinders their direct application. This study investigates and optimises data augmentation strategies to fine-tune LLMs to generate accurate, context-sensitive safety measures from structured coal mine risk records. The study systematically explored four experimental configurations, leveraging the Mistral-7B-Instruct model in conjunction with Quantised Low-Rank Adaptation (QLoRA) for efficient fine-tuning. These configurations comprised: (i) a baseline without augmentation, (ii) input-side lexical augmentation, (iii) output-side multi-reference augmentation, and (iv) a combined strategy. Performance was measured using BLEU, ROUGE, METEOR, and BERTScore metrics, along with statistical significance testing and qualitative analyses. The results show that, compared to other strategies, the input-side data augmentation strategy performs better. The findings indicate that input-side data augmentation yields significant improvements; this strategy increased the BERTScore (F1) from 0.360 to 0.530 and the BLEU score from 16.02 to 29.50 compared to the baseline model. In contrast, output-side multi-reference augmentation contributed to greater learning uncertainty and a consequent decline in performance. Statistical and qualitative analyses confirm that increasing input variety minimises model overfitting and enables the model to generate consistent, applicable, domain-specific safety measures. The proposed methodology provides a highly scalable solution for automated risk management in high-risk industrial environments, such as mining, offering a reliable, data-driven decision-support mechanism that minimises the limitations of manual review.