# MIRA-ChatGLM: A Fine-Tuned Large Language Model for Intelligent Risk Assessment in Coal Mining
## 5 Discussion
To ensure that MIRA-ChatGLM can be effectively applied at other mine sites, it is necessary to consider the specific conditions of each mine. The model's ability to provide personalized risk suggestions depends on the unique characteristics of the mine, such as environmental conditions, historical risk data, etc. When applying MIRA-ChatGLM to new mine sites, fine-tuning with local data may be required, including sensor readings, environmental factors, and risk patterns unique to that mine. This approach enables the model to adapt flexibly to different conditions, ensuring the relevance and accuracy of the risk assessment. Additionally, the model can be periodically retrained, incorporating new data to continuously improve the accuracy of risk suggestions as conditions change. During the fine-tuning process of the model, we employed LoRA and QLoRA methods from PEFT. Experimental results indicate that when the dataset size is small (fewer than \begin{table}
\begin{tabular}{c c c} \hline \hline
**Aspect** & **Existing Methods** & **Proposed Method** \\ \hline Risk assessment method & Rule-based expert systems that analyze sensor data and trigger alarms based on predefined thresholds [4]. & Integrated advanced pre-trained large language models (LLMs) for semantic interpretation and decision-making on multidimensional data. \\ Data type & Primarily single-modal data inputs (e.g., methane, carbon monoxide concentrations) [4]. & Supports multidimensional data integration, including gas concentrations, sensor locations, and equipment statuses. \\ Technological approaches & Traditional algorithms (e.g., random forest, SVM) and deep learning models (e.g., LSTM, GRUs) focused on specific patterns or time-series predictions [7,8]. & Utilizes instruction-tuned LLMs with strong generalization capabilities for complex and systematic risk assessments. \\ Data fusion & Optimes multidimensional sensor data to enhance robustness in risk assessment but faces challenges with high computational resource demands and limited annotated data [9, 10, 11, 12]. & Efficiently processes multidimensional data using pre-trained LLMs, reducing reliance on extensive annotated datasets through knowledge transfer. \\ Challenges addressed & Limited adaptability to complex scenarios, high computational demands of multidimensional data fusion, and insufficient annotated data [12]. & Enhanced adaptability to complex scenarios and alleviated reliance on extensive annotated datasets through pre-trained LLMs. \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison summary of existing models/technologies in the coal mining domain with our proposed solution. 3000 entries), the model's assessment performance is suboptimal. However, when the data volume reaches approximately 3000, the performance of the MIRA-ChatGLM model significantly improves, demonstrating excellent performance on evaluation metrics and achieving accurate and professional assessment results. Nevertheless, as the data volume continues to increase, the rate of performance improvement slows down, which may be related to data diversity. In comparing the fine-tuning processes of LoRA and QLoRA, we found that although LoRA consumes more hardware resources, the training time is significantly reduced, and the model exhibits faster inference speed with stronger professional response capabilities. Conversely, models fine-tuned with QLoRA take longer during training but require relatively fewer hardware resources, making it more manageable for most researchers. Additionally, we investigated the impact of the instruction data on model performance, and through a comprehensive analysis of automated metrics and manual evaluations, we validated the superiority of MIRA-ChatGLM. Despite the significant achievements of this study, there are also some limitations. Firstly, the base model is relatively small, and the limited amount of training data may lead to some errors in the content generated.