## 5 Discussion
### Limitations and Future Work  
While our study demonstrates the efficacy of using fine-tuned GPT-3.5 models for detecting misclassifications in crash data, several limitations must be acknowledged:  
* Imbalanced Class Distribution: The dataset was highly skewed toward non-construction zone crashes, which may have influenced the model's conservative predictions and low recall for the minority class (czone = 1).
* Low Recall in Misclassification Detection: Although the model achieved high precision in identifying mislabeled records, it only detected a small portion of the actual misclassifications, limiting its effectiveness in uncovering all inconsistencies.
* Limited Fine-Tuning Data: The fine-tuning process relied on only 100 labeled samples, which may have constrained the model's ability to generalize more nuanced or rare patterns across the broader dataset.
* Domain Dependency: The model was trained and tested on crash reports from a specific region (Missouri), and its performance may not directly transfer to other jurisdictions with different reporting styles or terminologies.  
To address these limitations and expand the framework's capabilities, future research could explore the following:  
* Model Comparisons and Baselines: Evaluate the framework using non-fine-tuned models (e.g., zero-shot GPT-3.5) as baselines, and benchmark against alternative LLMs, such as GPT-4 or open-source models like LLaMA, to assess generalizability and cost-performance trade-offs.
* Training Data Sensitivity: Investigate the impact of varying fine-tuning data sizes on performance, particularly to identify thresholds for reliable recall in low-resource scenarios.
* Class Imbalance Mitigation: Explore strategies to address skewed class distributions, such as oversampling, undersampling, or synthetic data generation (e.g., SMOTE or prompt-based data augmentation), to improve model performance on minority classes and increase recall for under-reported crash attributes.
* Prompt Engineering and Instruction Tuning: Design more targeted prompts or apply instruction-tuned variants of LLMs to better capture implicit work zone indicators and improve recall without compromising precision.
* Few-Shot and In-Context Learning: Apply few-shot examples at the inference time to enhance flexibility and support generalization across crash attributes without requiring additional fine-tuning.
* Hybrid Rule-LLM Models: Integrate traditional rule-based approaches with LLM outputs to balance precision and recall, enhancing robustness for misclassification detection.
* Multi-Label and Multi-Attribute Detection: Extend the framework to detect multiple crash attributes--such as injury severity, driver distraction, or pedestrian involvement--in a single processing pipeline.
* Cross-Dataset Validation: Test the framework on crash data from other states or jurisdictions to evaluate its transferability and regional adaptability.
* Integration with Crash Severity Simulation: Incorporating crash severity simulators, such as the one proposed by [PERSON] and [PERSON] (2013), could enhance future versions of this framework by linking mislabel detection to predicted outcomes [47]. This could support not only data validation, but also scenario-based policy testing and resource allocation.
* Multi-Label Classification: Future work could extend the framework to support multi-label classification, allowing for simultaneous detection of multiple misclassified crash attributes--such as injury severity, driver distraction, and road conditions--and thereby reflecting the complex nature of real-world crash events.  
These directions would not only strengthen the framework's accuracy and scalability, but also support the broader adoption of LLM-based validation tools in traffic safety analytics and policy.