# MIRA-ChatGLM: A Fine-Tuned Large Language Model for Intelligent Risk Assessment in Coal Mining
## 2 Related Work
### Current Research Status of Large Language Models (LLMs)  
In recent years, the rapid development of LLMs has significantly expanded the boundaries of natural language processing technology. These models have demonstrated tremendous potential across diverse application scenarios. For instance, the GPT-4 model has achieved notable advancements in text generation and contextual understanding [15], laying the foundation for the widespread application of pre-trained models. Simultaneously, LangChain technology has combined LLMs with knowledge graphs, greatly enhancing their reasoning and contextual processing capabilities when executing complex tasks [16]. Additionally, InstructGPT has integrated an instruction-following mechanism, substantially improving the model's ability to interpret and execute user directives [17]. In specialized fields, the application of LLMs is deepening. In the healthcare field, [PERSON] et al. 's research applied LLMs to the domain of traditional Chinese medicine product instructions (CPMI), developing the CPMI-ChatGLM model. This study explored the application of LoRA and P-Tuning v2 technologies through efficient fine-tuning of ChatGLM-6B, achieving significant performance improvements and demonstrating the potential of LLMs in the medical field, particularly in drug recommendations and diagnostic support [18]. In the financial industry, LLMs integrated with financial knowledge have been used to summarize contextual information in financial texts [19]. The education sector is also seeing the use of LLMs to create educational content, enhance student engagement and interaction, and personalize learning experiences [20]. Furthermore, research indicates that LLMs create unprecedented opportunities for large-scale analysis and the generation of language data, which play a central role across all areas of psychology, suggesting that LLMs could transform this field [21]. Some researchers are even using LLMs to develop multimodal models and autonomous driving systems [22], employing customized visualization instruction datasets and mixed fine-tuning strategies for autonomous vehicle applications [23]. Despite the significant achievements of LLMs and multi-agent systems in various fields, research and practice in the specific application of coal mine risk forecasting and assessment remain insufficient. Currently, gas monitoring and risk analysis in coal mines predominantly rely on manual operations, which consume substantial time and human resources, revealing clear limitations in processing real-time data in high-risk or complex environments. Although IoT technologies have been studied for safety monitoring, these studies often do not involve the application of advanced LLMs for predicting and analyzing potential risks in coal production. Addressing this research gap, our proposed MIRAChatGLM project aims to combine LLMs with historical alarm data accumulated from sensors at various locations in coal mines, exploring the application potential of LLMs in risk assessment. This innovative approach is expected to significantly enhance the efficiency and accuracy of risk assessments in complex coal production scenarios, providing a more robust support for safety in mining operations. In summary, this study makes the following contributions:  
1. We propose a language model specific to the field of coal mine risk assessment, named MIRA-ChatGLM, marking the first LLM designed for mining risk evaluation. 2. We employ a high-quality risk assessment (RA) dataset for fine-tuning the base model with instructions. 3. A high-quality RA dataset has been constructed based on historical alarm data from gas sensors at coal production sites, encompassing various measurement types such as carbon monoxide (CO), laser methane (CH4), carbon dioxide (CO2), and oxygen (O2). Our goal is to provide valuable resources for research and application in the field of mining risk assessment.