unstructured narrative descriptions are rich in detail but difficult to process at scale. Prior studies have highlighted widespread inconsistencies between coded labels and textual narratives, often leading to under-reporting or misclassification [4]. Traditional methods--such as rule-based systems or shallow classifiers--struggle to resolve these inconsistencies, especially in imbalanced datasets.  
Traffic conditions in work zones are influenced by dynamic variables such as the weather, vehicle speed, and detour complexity [5]. Traditional assessments often overlook these broader implications, including travel delay and fuel consumption, which are vital for accurate crash modeling [3]. Integrating such variables reinforces the need for contextual models capable of interpreting complex crash environments.  
Meanwhile, advances in machine learning and natural language processing (NLP) have improved real-time crash prediction and severity modeling [6]. Yet, most research has focused on forward-looking tasks--such as predicting crash likelihood or simulating traffic flow--rather than retrospectively auditing the quality of existing datasets. The potential of large language models (LLMs) to bridge structured and narrative data for retrospective validation remains underexplored. In this study, we selected GPT-3.5 for its strong balance of fine-tuning capability, task-specific accuracy, and computational efficiency [7].  
This study addresses the abovementioned research gap by proposing a multimodal, AI-assisted framework for detecting mislabeled crash records. By fusing structured crash attributes with unstructured narratives and fine-tuning a GPT-3.5 model, the framework flags inconsistencies between coded and inferred work zone labels. Unlike previous work that treats LLMs solely as predictive tools, our approach repositions them as validation instruments capable of surfacing high-confidence anomalies for expert review.  
The contributions of this study are threefold:  
1. We propose a scalable framework that repurposes a fine-tuned GPT-3.5 model for retrospective data validation--shifting the use of large language models (LLMs) from prediction to auditing in the context of traffic safety.
2. We demonstrate the effectiveness of multimodal data fusion by integrating structured crash attributes and unstructured narrative descriptions to detect inconsistencies in construction zone labeling.
3. We highlight the framework's generalizability, suggesting its potential to identify other types of misclassified attributes (e.g., injury severity, pedestrian involvement, distraction) in crash datasets.  
This precision-oriented, semi-automated framework offers a promising solution for improving the reliability of traffic crash datasets--ultimately supporting more accurate policy decisions and safety interventions. Accordingly, this study is guided by the following research question:  
Can fine-tuned large language models (LLMs), when applied to multimodal crash data combining structured attributes and narrative text, effectively detect mislabeled construction zone crashes and support data quality auditing?  
The remainder of this paper is organized as follows: Section 2 reviews prior work on work zone misclassification, data quality challenges, and LLM applications in transportation; Section 3 outlines the methodology; Section 4 presents the results; Section 5 offers a discussion; and Section 6 concludes with implications and future research directions.