## 7 Simulation Result
### Performance Analysis and Discussion  
This section provides a quantitative analysis of the best-performing MLLM, the best-performing MLLM with PES, and the degree of performance enhancement, based on the previously presented results across datasets, MLLM models, and evaluation metrics. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Method** & **Phii** & **Llama** & **Pixtral** & **Qwen** & **Phii** & **Llama** & **Pixtral** & **Qwen** & **Phii** & **Llama** & **Pixtral** & **Qwen** \\ \hline
**Metric** & \multicolumn{4}{c}{**S-BERT**} & \multicolumn{4}{c}{**MoverScore**} & \multicolumn{4}{c}{**CIDEr**} \\ \hline B & 0.2142 & 0.117 & 0.0531 & 0.2589 & 0.3249 & 0.2768 & 0.2362 & 0.3378 & 0.0358 & 0 & 0.0002 & 0.0425 \\ \hline I & **0.5981** & 0.375 & 0.4709 & 0.079 & **0.6897** & 0.4306 & 0.3946 & 0.2444 & **0.8707** & 0.16 & 0.1065 & 0 \\ \hline I, S & 0.3326 & 0.336 & 0.4537 & 0.0671 & 0.4665 & 0.4119 & 0.3944 & 0.2383 & 0.2081 & 0.1259 & 0.0676 & 0 \\ \hline I, T & 0.4935 & **0.3869** & **0.4814** & 0.0615 & 0.4782 & **0.4431** & 0.3966 & 0.2298 & 0.0328 & **0.2025** & 0.0986 & 0 \\ \hline C & 0.1225 & 0.2049 & 0.2564 & 0.2526 & 0.2865 & 0.3186 & 0.3738 & 0.3507 & 0.0766 & 0.026 & 0.1221 & 0.1213 \\ \hline C, I & 0.3134 & 0.216 & 0.2055 & 0.0699 & 0.3519 & 0.3174 & 0.3263 & 0.2367 & 0.0015 & 0.0442 & 0.0005 & 0 \\ \hline C, S & 0.1492 & 0.2255 & 0.2449 & 0.2373 & 0.2984 & 0.3294 & 0.353 & 0.3425 & 0.032 & 0.0514 & 0.1022 & 0.0933 \\ \hline C, T & 0.26 & 0.2184 & 0.3335 & 0.3237 & 0.3916 & 0.3255 & 0.4352 & 0.4032 & 0.164 & 0.0201 & 0.2885 & 0.2635 \\ \hline S & 0.1673 & 0.2193 & 0.4048 & **0.3998** & 0.3318 & 0.325 & **0.4849** & **0.4625** & 0.0442 & 0.03 & **0.3412** & **0.4056** \\ \hline T & 0.1472 & 0.292 & 0.4374 & 0.3427 & 0.3127 & 0.3574 & 0.4877 & 0.4217 & 0.0115 & 0.065 & 0.3272 & 0.3967 \\ \hline R(B) & 0.4848 & 0.2513 & 0.3764 & 0.1443 & 0.5856 & 0.3335 & 0.3697 & 0.2854 & 0.6442 & 0.0543 & 0.0972 & 0.0376 \\ \hline R(C) & 0.1422 & 0.2068 & 0.1263 & 0.1302 & 0.2863 & 0.3119 & 0.2785 & 0.2686 & 0.0387 & 0.0372 & 0.0082 & 0.0061 \\ \hline R(S) & 0.1197 & 0.2038 & 0.2239 & 0.0821 & 0.2932 & 0.3169 & 0.3408 & 0.2562 & 0.0519 & 0.0406 & 0.0139 & 0 \\ \hline R(T) & 0.1246 & 0.2022 & 0.0155 & 0.1151 & 0.2771 & 0.3045 & 0.2346 & 0.263 & 0.0004 & 0.0092 & 0 & 0.0012 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Performances of BLEU, ROUGE, and METEOR according to MLLM models and prompt engineering techniques on the CVBench dataset (The boldfaced numbers indicate the highest performance for each language model across different PES variants). \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Method** & **Phii** & **Llama** & **Pixtral** & **Qwen** & **Phii** & **Llama** & **Pixtral** & **Qwen** & **Phii** & **Llama** & **Pixtral** & **Qwen** \\ \hline
**Metric** & \multicolumn{4}{c}{**BLEU**} & \multicolumn{4}{c}{**ROUGE**} & \multicolumn{4}{c}{**METEOR**} \\ \hline B & **1.1247** & 0.3936 & 0.245 & 1.0894 & 0.0625 & 0.0236 & 0.0152 & 0.0571 & 0.125 & 0.0872 & 0.032 & 0.1231 \\ \hline I & 0.5495 & **1.8268** & **2.4048** & 0.043 & **0.4529** & 0.1511 & 0.1244 & 0.003 & **0.2598** & 0.182 & 0.1701 & 0.0108 \\ \hline I, S & 0.8242 & 1.5994 & 1.817 & 0.0521 & 0.2778 & 0.1149 & 0.1119 & 0.0037 & 0.19 & 0.1695 & 0.157 & 0.0087 \\ \hline I, T & 0.4577 & 1.5656 & 2.1895 &Table 16 presents the optimal prompt engineering strategies (PES) and MLLM models across various datasets and evaluation metrics, derived from the results in Tables 4-15. As observed in the results, the optimal PES varies significantly depending on the dataset and the chosen MLLM model. Generally, for general category datasets, ICL, ToT, and RAG are predominantly utilized. This trend can be attributed to the characteristics of multimodal data, where generating captions from recognized objects and input text requires in-context reasoning, multi-path inference, and knowledge expansion to deepen the relationship between objects and textual context.