## 5 Discussion  
The authors developed a novel fine-tuned GPT model to predict construction accident outcomes. The GPT mode excels in tasks involving accident-related text data by capturing context, and employing end-to-end learning without the manual feature engineering of traditional machine learning and deep learning algorithms. Additionally, the introduction of a saliency visualization method enhances the interpretability of the model's decision-making process. The research makes a substantial contribution to the domain of construction safety through the application of NLP models, with a particular emphasis on utilizing generative AI models. The findings of the study underscore the superior performance of fine-tuned GPT models, specifically highlighting the effectiveness of GPT-3.0, when compared to traditional models such as TF-IDE, CNN, BERT, and GPT-2.0. The achieved overall accuracy of 82.33% signals the potential of the fine-tuned GPT model for predicting construction accident types from unstructured text data. The proposed saliency visualization method is a noteworthy addition to the research, offering a solution to the black-box nature of GPT models. By systematically assessing the impact of individual words on prediction outcomes, the method enhances interpretability, suggesting unique insights into the decision-making process of the model. This aspect of the research contributes to building trust in the model's predictions, minimizing the labor required to extract valuable insights from free-form text, and generating dependable safety predictions from accident reports. It also has great potential to understand accident causal relationships that support existing correlational evidence in attribute-based accident analytics studies [15, 23]. Furthermore, this research offers actionable insights for practical application at construction sites. Implementation involves incorporating construction documents (e.g., daily work reports and text data regarding scheduling, estimating, and specifications) to proactively predict potential accidents. By utilizing easy-to-use generative AI at construction job sites, it becomes possible to automatically assess the anticipated accident types and discern the influential attributes or textual elements. This approach facilitates a preventive strategy by leveraging the model's predictive capabilities to mitigate safety risks. Such integration of advanced LLM models into daily construction safety management holds promise for enhancing on-site safety measures. While this study presents significant advancements in the prediction of construction accidents through fine-tuned GPT models, several limitations warrant consideration. The fine-tuning process of the GPT models on a specific dataset raises concerns about its adaptability to unforeseen circumstances and diverse construction environments. Incorporating multimodal data, such as images and videos from construction sites, could further enrich the model's learning capabilities and provide a more comprehensive understanding of accidents. Additionally, the downsampling approach employed to address data imbalance, while effective, may inadvertently omit crucial information related to infrequent accident types, impacting the model's robustness. Furthermore, the study's focus on the Korean construction context may limit the generalizability of the findings to construction sites in other geographical regions with distinct safety reporting practices and cultural nuances. Including and testing more data from different countries and languages can mitigate the generalizability and reliability concerns of the model. Separately, the study found that GPT-3.0 consistently outperformed GPT-2.0, mainly due to its substantial increase in parameters, with 175 billion compared to GPT-2.0's 1.5 billion. This increased scale allowed GPT-3.0 to capture more intricate patterns, resulting in significantly improved performance.