# Comparative Analysis of BERT and GPT for Classifying Crisis News with Sudan Conflict as an Example
## 6 Discussion  
The experimental results demonstrate that both BERT and GPT exhibit distinct strengths and weaknesses in SL-MLG and MLC tasks, with differences in precision, recall, accuracy, computational efficiency, and adaptability. The choice between the two depends on the specific classification requirements, the need for model fine-tuning, and the constraints of the computational resources. Tables 8 and 9 summarize the highest-performing algorithm from each table presented in the preceding sections. Furthermore, to support a fair and statistically robust evaluation, the BERT models were tested using both a traditional 90/10 training-testing split and 5-fold cross-validation. The initial 90/10 experiments provided a fast baseline and enabled hyperparameter tuning via Optuna, resulting in improved F1-scores but at the cost of a longer runtime. However, due to concerns about the same small test set size (43 articles), we introduced 5-fold cross-validation for both the MLC and SL-MLG tasks using bert-base and bert-large. This approach yielded higher and more stable performance metrics, particularly for bert-large, and confirmed that BERT performance scales reliably with model size. To ensure computational feasibility, hyperparameter tuning was excluded from the cross-validation runs, although the average fold runtimes remained manageable (9-31 min). This comprehensive evaluation strategy ensured that our conclusions about BERT model performance mitigated concerns regarding statistical significance and generalizability. For both the SL-MLG and MLC tasks, larger models (e.g., Llama3.1-70b, Gemma2-27b, and bert-large) consistently outperformed their smaller counterparts (e.g., Llama3.1-7b, Gemma2-9b, and bert-base). This suggests that higher-parameter models benefit from increased contextual retention, enabling better generalization in complex classification tasks. For example, for MLC, Llama3.1-70b under RAG achieved an F1-score of 68.80%, outperforming Llama3.1-7-b (57.22%). Similarly, Gemma2-27b under RAG ICL showed a superior F1-score (62.21%) compared to Gemma2-9b (53.03%). This trend suggests that higher-parameter models are better suited for handling nuanced, multi-label classification tasks, likely due to their enhanced capacity to model overlapping semantic categories. A similar trend was observed in SL-MLG, where Llama3.1-70b achieved the highest accuracy (90.70%), surpassing Llama3.1-7b (86.05%). Likewise, Gemma2-27b (86.05% accuracy) outperformed Gemma2-9b (79.07%). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**METHOD** & **MODEL** & **TUNING** & **TRAINING** & **INFERENCE** & **ACC.** \\ \hline
**RAG ICL** & Llama3.3-70b & & 3 m 12 s & **90.70\%** \\
**RAG ICL** & Llama3.1-70b & & 3 m 14 s & **90.70\%** \\
**RAG** & Llama3.3-70b & & 1 m 44 s & 86.05\% \\
**Zero-Shot** & Llama3.3-70b & & 40 s & 76.74\% \\
**BERT hp-tuned** & bert-base-uncased & 137 m 57 s & 7 m 20 s & 1 s & 74.42\% \\
**BERT hp-tuned** & bert-large-uncased & 1170 m 9 s & 20 s & 83.72\% \\
**BERT** & bert-base-uncased & 10 m 4 s & 1 s & 76.74\% \\
**BERT** & bert-large-uncased & & 28 m 29 s & 2 s & 74.42\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Collective SL-MLG experimental results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**METHOD** & **MODEL** & **TUNING** & **TRAINING** & **INFERENCE** & **F1.** \\ \hline
**RAG ICL** & Llama3.1-70b & & 3 m 30 s & 67.00\% \\
**RAG** & Llama3.1-70b & & 2 m 30 s & 68.80\% \\
**Zero-Shot** & Llama3.3-70b & & 1 m 31 s & 62.75\% \\
**BERT hp-tuned** & bert-base-uncased & 181 m 51 s & 5 m 54 s & 18 s & 63.01\% \\
**BERT hp-tuned** & bert-large-uncased & 1190 m 22 s & 160 m 33 s & 19 s & **68.90\%** \\
**BERT** & bert-base-uncased & & 9 m 41 s & 2 s & 62.85\% \\
**BERT** & bert-large-uncased & & 14 m 54 s & 1 s & 54.62\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Collective MLC experimental results. These results suggest that higher-parameter models improve classification accuracy, reinforcing their advantages in structured classification tasks.