## 2 Research Background  
The adoption of NLP analysis presents a transformative shift in the realm of construction site safety [23]. NLP, equipped with its ability to sift through extensive textual data, provides a robust framework for uncovering subtle patterns and correlations within accident records [24; 25]. This enables stakeholders to access distinctive insights into the fundamental reasons behind accidents, enabling the creation of proactive and specific safety measures to avert the recurrence of similar incidents [26; 27]. By leveraging the capabilities of NLP, construction site managers and safety experts can optimize their decision-making processes, bolstering the efficacy of accident analysis and fortifying the overall safety standards within the construction industry [28]. This transition to NLP-driven analysis signifies a progressive stride toward a more data-driven and proactive approach to accident prevention, ultimately fostering a safer working environment for construction personnel and mitigating potential risks associated with complex construction operations. Specifically, recent developments in NLP have created novel opportunities for the automated examination of textual records related to accidents [17; 23; 29; 30]. By applying NLP techniques, pertinent information can be extracted from unstructured free-text data, enabling an effective categorization of accidents based on various parameters [31]. Numerous studies have emphasized the efficacy of NLP in automating accident classification, resulting in improved efficiency and reduced prejudice. Table 1 presents a summary of the NLP models used in the literature to predict construction accidents. Specifically, [PERSON] et al. [32] demonstrated analyzing unstructured incident reports utilizing the NLP model, yielding significant results with F1 score values of 0.96, respectively. Similarly, [PERSON] et al. [16] employed text mining and NLP methods to investigate construction accident reports, utilizing various machine learning models, with the optimized ensemble model showcasing the highest F1 score of 0.68. [PERSON] et al. [33] introduced the Symbiotic Gated Recurrent Unit for the classification of construction site accidents, achieving an average weighted F1 score of 0.69, outperforming other AI techniques. Additionally, [PERSON] & [PERSON] [34] developed a system for managing construction accident knowledge, demonstrating notable recall, precision, and F1 score values of 0.71, 0.93, and 0.80, respectively. These research efforts emphasize the importance of employing advanced NLP-based modeling techniques to gain a comprehensive understanding of the various factors influencing accidents and to improve safety protocols. However, as mentioned in the Introduction, current studies have predominantly focused on developing conventional machine and deep learning methods, which entail manually extracting text features and inputting them into a classifier [15; 29; 36]. These approaches come with inherent limitations, including limited scalability and generalization, difficulties in analyzing free-form text, high dimensionality, and a lack of adaptability to new tasks. While traditional machine and deep learning methods have made strides in NLP within the construction domain, overcoming these limitations requires the incorporation of more innovative, generative, and robust AI models. Recently, LLMs, particularly those built on the transformer architecture such as GPT, developed by OpenAI, present notable advantages for various NLP tasks, including classification, compared to traditional machine learning and deep learning models [20; 37]. These models are trained on vast datasets, enabling them to generate human-like text based on the input they receive. The adaptability and proficiency of GPT models in processing and generating natural language have the potential to significantly impact and facilitate various aspects in diverse fields, ranging from education and customer service to research and industries.