## 4 Results  
Prior to assessing the models' effectiveness in correcting errors, we first evaluated their proficiency in detecting errors, as accurate detection is crucial to the overall quality of the error correction process. The results of the models are summarized in Table 2.  
The pretrained GBERT and fine-tuned GBERT exhibit notable differences in their error detection performance. The pretrained GBERT has an accuracy rate of 0.65, with a precision of 0.06, recall of 0.24, and an F1 score of 0.09. In contrast, the fine-tuned GBERT showed improved accuracy at 0.82, though its precision remained low at 0.07 and recall slightly decreased to 0.19, leading to a marginal increase in the F1 score to 0.10. These results demonstrate that fine-tuning enhances the model's overall accuracy by substantially lowering the rate of false positives and negatives, although it slightly affects the precision and recall. The comparison between the pretrained and fine-tuned GELECTRA models highlights notable differences in their performance for error detection. The pretrained model achieves an accuracy of 0.43, a precision of 0.06, and a recall of 0.48, resulting in an F1 score of 0.11. The fine-tuned model demonstrates significant improvement, with an accuracy of 0.64, precision of 0.19, recall of 0.38, and an F1 score of 0.26. This improvement indicates a better balance between precision and recall, though GELECTRA's performance still lags behind GBERT in terms of accuracy. Among all models, GPT-4o and Llama models demonstrated the best performance in error detection. GPT-4o achieved an accuracy of 0.97, a precision of 0.88, and a recall of 0.83, resulting in an F1 score of 0.86. This reflects its ability to effectively detect errors while maintaining high precision. The Llama models followed closely, with Llama-3.2-1B achieving an accuracy of 0.92, but with a much lower precision of 0.26 and recall of 0.63, leading to an F1 score of 0.36. Llama-3.2-3B performed better with an accuracy of 0.96, precision of 0.78, recall of 0.81, and F1 score of 0.80. Llama-3.1-8B achieved an accuracy of 0.97, precision of 0.84, recall of 0.85, and an F1 score of 0.85, demonstrating a more balanced performance between precision and recall. The best-performing model was Llama-3.1-70B, with an accuracy of 0.98, precision of 0.87, recall of 0.89, and an F1 score of 0.88, making it the most robust model in terms of both precision and recall. Based on these results, we proceeded to the grammatical error correction stage with GPT-4o, Llama-3.1-8B, and Llama-3.1-70B, as they exhibited the most consistent and reliable performance in error detection. The results in Table 3 show that GPT-4o achieves the highest average correction accuracy at 92.52%, maintaining strong performance across most documents, with a peak of 97.26% (Document 7) and a low of 50.00% (Document 2). Llama-3.1-70B follows closely with 89.68% accuracy, also performing well across documents, ranging from 96.77% (Document 3) to 50.00% (Document 2). Both models demonstrate reliability in grammatical error correction, with slight fluctuations based on text complexity. In contrast, Llama-3.1-8B shows greater variability, averaging 76.92%. While it achieves a perfect 100.00% accuracy in Document 2, it drops significantly to 14.29% in Document 4, indicating inconsistency in handling diverse linguistic structures. Overall, GPT-4o and Llama-3.1-70B are the most reliable for error correction, while Llama-3.1-8B's fluctuating performance suggests it may require further fine-tuning for stability.