While Generative Pre-Trained Transformers (GPT)-based models offer high potential for context-specific information generation, inaccurate numerical responses, a lack of detailed information, and hallucination problems remain as the main challenges for their use in assisting safety engineering and management tasks. To address the challenges, this paper systematically evaluates the effectiveness of the Retrieval-Augmented Generation-based GPT (RAG-GPT) model for generating detailed and specific construction safety information. The RAG-GPT model was compared with four other GPT models, evaluating the models' responses from three different groups––2 researchers, 10 construction safety experts, and 30 construction workers. Quantitative analysis demonstrated that the RAG-GPT model showed superior performance compared to the other models. Experts rated the RAG-GPT model as providing more contextually relevant answers, with high marks for accuracy and essential information inclusion. The findings indicate that the RAG strategy, which uses vector data to enhance information retrieval, significantly improves the accuracy of construction safety information.

中文翻译：

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检索基于增强生成的大型语言模型生成施工安全信息的有效性


虽然基于生成式预训练转换器 （GPT） 的模型为生成特定上下文的信息提供了很高的潜力，但不准确的数值响应、缺乏详细信息和幻觉问题仍然是它们在辅助安全工程和管理任务中使用的主要挑战。为了应对这些挑战，本文系统地评估了基于 Retrieval-Augmented Generation-based GPT （RAG-GPT） 模型在生成详细和具体的施工安全信息方面的有效性。将 RAG-GPT 模型与其他四个 GPT 模型进行比较，评估三个不同组（2 名研究人员、10 名建筑安全专家和 30 名建筑工人）的模型响应。定量分析表明，与其他模型相比，RAG-GPT 模型表现出更好的性能。专家将 RAG-GPT 模型评为提供了更上下文相关的答案，在准确性和基本信息包含方面得分很高。研究结果表明，使用矢量数据增强信息检索的 RAG 策略显著提高了施工安全信息的准确性。