Automatic Generation of Job Safety Reports with Explainable RAG-Based LLMs
提出一种结合检索增强生成与可解释大语言模型的框架，从非结构化事故描述中自动生成作业安全报告，在航空安全数据集上F1最高达0.909，可辅助安全专家分析事故根因。
Abstract Monitoring workplace activities is critical for ensuring job safety. Generative Artificial Intelligence (Gen-AI) and Human-centered Artificial Intelligence (Hum-AI) can suggest new trustworthy solutions to automate these monitoring procedures, ensuring improved work accident prevention. In this paper, we present a novel framework that combines Retrieval Augmented Generation (RAG) with explainable LLMs to automatically generate job safety reports from unstructured accident descriptions. Our method integrates embeddings like BERT and SciBERT and explainable AI exploiting Layer-Wise Relevance Propagation (LRP) to highlight root causes of accidents within the generated reports. We evaluate multiple LLMs, including LLaMA 3.1, Mixtral-8x7B, and DeepSeek v2, on the Aviation Safety Reporting System (ASRS) dataset. Results show that our best configuration (Mixtral-8x7B with SciBERT) achieves F1-scores up to 0.909 and GLEU and METEOR scores above 0.3 and 0.2. These findings demonstrate the effectiveness and interpretability of the proposed system in real-world job safety contexts and how the proposed approach could assist safety experts or inspectors more explicitly.