# Artificial Intelligence vs. Human: Decoding Text Authenticity with Transformers
## 4 Results
The metrics include accuracy (Acc), precision (P), recall (R), F-score (F-score), and model runtime (in minutes). These results highlight important trade-offs between model complexity, performance, and computational efficiency. **RoBERTa-large** stands out with the highest F-score and accuracy across both datasets, achieving 83.00% accuracy and an impressive 83.50% F-score on the M4 dataset. This model's superior performance underscores its effectiveness in distinguishing between human-written and AI-generated text. The higher accuracy and F-score are indicative of its robust capacity to capture and represent nuanced textual features, making it a powerful tool for this task. However, this performance comes with a trade-off in computational cost, as reflected in its runtime of 607 min, which may be a consideration in resource-constrained environments. **RoBERTa-base** presents a balanced alternative, offering a commendable performance with 81.00% accuracy and an 81.99% F-score on the M4 dataset. Its computational efficiency is also notable, with a significantly shorter runtime of 166 min compared to RoBERTa-large. This makes RoBERTa-base a viable option when a balance between performance and resource use is required. **BERT-base** and **DistilBERT-base-uncased** show lower performance metrics relative to RoBERTa models. **BERT-base** achieves 71.00% accuracy and a 72.47% F-score on the M4 dataset, while **DistilBERT-base-uncased** performs slightly worse with 68.00% accuracy and a 68.47% F-score. Despite their lower performance, these models offer significant advantages in terms of computational efficiency. **DistilBERT-base-uncased** in particular, with a runtime of just 77 min, is highly resource-efficient, making it suitable for scenarios where computational resources are limited. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Acc (\%)**} & \multirow{2}{*}{**P (\%)**} & \multirow{2}{*}{**R (\%)**} & \multirow{2}{*}{**F-Score (\%)**} & \multirow{2}{*}{**Model**} \\  & & & & & & \\ \hline Baseline & - & 69.00 & & & & \\ XLM-RoBERTa-base & Indonesia Hoax & 68.00 & 70.00 & 68.00 & 68.99 & 522 \\  & News Detection & 67.50 & 69.00 & 67.00 & 67.99 & \\ BERT-base-multilingual- & Indonesian Hoax & 63.00 & 68.00 & 64.00 & 65.94 & 415 \\  & News Detection & 70.00 & 71.00 & 71.00 & 71.00 & 203 \\  & News Detection & 70.00 & 70.00 & 68.00 & 68.99 & \\  & TURNBACKHOAX & 69.00 & 70.00 & 68.00 & 68.99 & \\  & Dataset & **72.00** & **73.00** & **72.00** & **72.50** & 400 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance metrics for multilingual subtask. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Dataset** & **Acc (\%)** & **P (\%)** & **R (\%)** & **F-Score (\%)** & **Model Runtime (min.)** \\ \hline Baseline & - & 74.00 & & & & \\
**RoBERTa-large** & M4 & **83.00** & **84.00** & **83.00** & **83.50** & 607 \\  & AICrowd & 82.50 & 83.50 & 82.00 & 82.74 & \\ RoBERTa-base & M4 & 81.00 & 83.00 & 81.00 & 81.99 & 166 \\  & AICrowd & 79.50 & 80.00 & 78.00 & 78.99 & \\ BERT-base & M4 & 71.00 & 74.00 & 71.00 & 72.47 & 162 \\  & AICrowd & 68.00 & 70.00 & 67.00 & 68.47 & \\ DistilBERT-base-uncased & M4 & 68.00 & 73.00 & 68.00 & 70.41 & 77 \\  & AICrowd & 65.00 & 67.00 & 64.00 & 65.47 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance metrics for monolingual subtask. The performance variations between the datasets (M4 and AICrowd) highlight the importance of dataset diversity in evaluating model robustness. Different datasets can present varied linguistic characteristics and complexities, which can impact model performance. The consistent trends observed across the datasets for each model reinforce their relative strengths and limitations, providing valuable insights for selecting the most appropriate model based on specific requirements and constraints.