# Comparative Analysis of BERT and GPT for Classifying Crisis News with Sudan Conflict as an Example
## 5 Results
### Mlc
This highlights a key trade-off between computational cost and classification performance, where BERT incurs higher computational overhead due to tuning, training, and inference, while GPT achieves rapid inference but relies heavily on prompt engineering for classification accuracy. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **MODEL** & **TUNING** & **TRAINING** & **INFERENCE** & **ACC.** & **PREC.** & **REC.** & **F1** \\ \hline
**RAG ICL** & \multirow{2}{*}{Gemma2-9k} & 1 m 17 s & 13.95\% & 65.89\% & 48.26\% & 53.03\% \\  & & 1 m 54 s & 18.60\% & 77.52\% & 56.40\% & 62.21\% \\  & Llama3.3-70b & 3 m 56 s & 6.98\% & 59.34\% & 77.13\% & 64.42\% \\  & & 11 m 42 s & 0.00\% & 26.87\% & 84.50\% & 36.36\% \\  & & 3 m 30 s & 25.58\% & 78.49\% & 63.37\% & 67.00\% \\  & & 11 m 22 s & 6.98\% & 49.08\% & 67.05\% & 50.98\% \\  & & 11 m 18 s & 11.63\% & 67.44\% & 50.39\% & 53.88\% \\ \hline
**RAG** & \multirow{2}{*}{Gemma2-9k} & 1 m 12 s & 9.30\% & 61.24\% & 63.76\% & 59.35\% \\  & & 11 m 40 s & 9.30\% & 61.05\% & 61.43\% & 57.88\% \\  & & 2 m 36 s & 11.63\% & 62.02\% & 80.62\% & 67.30\% \\  & & 11 m 6 s & 2.33\% & 2.33\% & 2.33\% & 2.33\% \\  & & 2 m 30 s & 16.28\% & 70.74\% & 73.84\% & 68.08\% \\  & & 11 m 12 s & 13.95\% & 51.16\% & 72.29\% & 57.22\% \\  & & 11 m 8 s & 9.30\% & 53.91\% & 59.88\% & 52.04\% \\ \hline
**Zero-Shot** & \multirow{2}{*}{Gemma2-9k} & 32 s & 11.63\% & 55.04\% & 57.56\% & 53.48\% \\  & & 48 s & 20.93\% & 59.69\% & 55.81\% & 53.22\% \\  & & 11m 31 s & 9.30\% & 62.95\% & 71.51\% & 62.75\% \\  & & 22 s & 6.98\% & 37.91\% & 43.22\% & 37.69\% \\  & & 11m 21 s & 16.23\% & 66.05\% & 63.37\% & 60.79\% \\  & & 25 s & 11.63\% & 46.88\% & 52.91\% & 45.82\% \\  & & 28 s & 6.98\% & 41.30\% & 50.19\% & 41.73\% \\ \hline
**BERT hp-tuned** & \multirow{2}{*}{bert-base-uncased} & 181 m 51 s & 5 m 54 s & 18 s & 34.88\% & 72.61\% & 57.78\% & 63.01\% \\  & & 1190 m 22 s & 160 m 33 s & 19 s & **41.86\%** & 75.70\% & 64.44\% & **68.90\%** \\ \hline
**BERT** & \multirow{2}{*}{bert-base-uncased} & 9 m 41 s & 2 s & 30.23\% & 71.18\% & 57.78\% & 62.85\% \\  & & 14 m 54 s & 1 s & 27.91\% & 64.32\% & 52.22\% & 54.62\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: MLC results of GPT and BERT models. Total runtime is split into three columns: TUNING, TRAINING, and INFERENCE. Only BERT models have tuning and training times; GPT models rely on prompt-based inference, explaining the blank entries in the TUNING and TRAINING columns. Table 6 presents an example from Llama3.3-70b under RAG ICL, in which the model achieved a perfect multi-label classification. This example illustrates the model's ability to correctly identify all relevant categories in a conflict narrative, aligning with its high F1-score and strong recall score in the overall MLC results. Furthermore, it demonstrates how larger-parameter models like Llama3.3-70b outperform smaller models such as Llama3.1-7b, which achieved an F1-score of only 50% on the same example because it missed two of the three ground-truth categories: Willful killing of civilians and Indiscriminate use of weapons.