## 7 Simulation Result
### nocaps  
As observed in Tables 8 and 9, the baseline performance (B) analysis reveals that Qwen-2 achieves the highest scores in BLEU, ROUGE, and METEOR, indicating that it possesses the strongest baseline performance in caption generation. In contrast, Pixtral outperforms the other models in MoverScore and CIDEr, while Llama 3.2 achieves the highest score in S-BERT, demonstrating its strength in semantic similarity evaluation.  
When applying the Greedy PES, the optimal model \(m^{*}\) and optimal PE strategy \(p^{*}\) are found to be Qwen-2 with the ToT technique. However, it is noteworthy that Phi-3.5, despite  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Method** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **Qwen** & **Phi** & **[PERSON]** & **[PERSON]** & **Qwen** & **Phi** & **[PERSON]** & **[PERSON]** & **Qwen** \\ \hline
**Metric** & \multicolumn{4}{c}{**BLEU**} & \multicolumn{4}{c}{**ROUGE**} & \multicolumn{4}{c}{**METEOR**} \\ \hline I, T & 18.6258 & 3.3325 & 3.5435 & 5.2165 & 0.4578 & 0.1781 & 0.1738 & 0.2596 & 0.4658 & 0.2619 & 0.329 & 0.2159 \\ \hline C & 19.4097 & 7.898 & 11.3782 & 16.8317 & 0.4651 & 0.3209 & 0.3706 & 0.4701 & 0.5053 & **0.3908** & 0.4233 & 0.5038 \\ \hline C, I & 20.726 & 6.0268 & 5.2859 & 16.5979 & 0.4941 & 0.259 & 0.2405 & 0.45 & 0.5269 & 0.3442 & 0.3241 & 0.4891 \\ \hline C, S & 16.7199 & 7.1802 & 14.3171 & 12.4918 & 0.4375 & 0.2621 & 0.4189 & 0.4278 & 0.483 & 0.343 & 0.4733 & 0.4599 \\ \hline C, T & 16.0005 & 3.6898 & 2.5275 & 16.6957 & 0.449 & 0.1944 & 0.0986 & 0.4487 & 0.5047 & 0.2851 & 0.1634 & 0.5029 \\ \hline S & 16.4494 & 3.9303 & 2.6445 & 15.035 & 0.4487 & 0.1971 & 0.1532 & 0.4612 & 0.4882 & 0.3016 & 0.2352 & 0.4953 \\ \hline T & 19.5581 & 4.1068 & **17.6505** & **18.9299** & 0.4815 & 0.219 & **0.4659** & **0.5173** & 0.5141 & 0.3094 & **0.5154** & **0.5476** \\ \hline R(B) & **28.1474** & 8.6846 & 4.5104 & 5.5115 & 0.5263 & 0.3452 & 0.2136 & 0.2318 & 0.4993 & 0.3499 & 0.377 & 0.2185 \\ \hline R(C) & 17.3322 & 6.31 & 4.038 & 4.6716 & 0.447 & 0.2656 & 0.2017 & 0.212 & 0.4889 & 0.3262 & 0.3387 & 0.2057 \\ \hline R(S) & 2.4731 & 6.6279 & 2.9527 & 5.1781 & 0.1029 & 0.3315 & 0.1606 & 0.2426 & 0.1093 & 0.334 & 0.2594 & 0.2226 \\ \hline R(T) & 17.693 & 3.1555 & 4.2711 & 4.7382 & 0.4538 & 0.171 & 0.2078 & 0.2375 & 0.4651 & 0.237 & 0.3394 & 0.214 \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **Qwen** & **Phi** & **[PERSON]** & **[PERSON]** & **Qwen** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **Qwen** \\ \hline
**Metric** & \multicolumn{4}{c}{**S-BERT**} & \multicolumn{4}{c}{**MoverScore**} & \multicolumn{4}{c}{**CIDEr**} \\ \hline B & 0.2602 & 0.6638 & 0.644 & 0.6103 & 0.5109 & 0.7425 & 0.7459 & 0.6822 & **0.0375** & 0.0001 & 0.0956 & **0.1313** \\ \hline I & 0.6542 & 0.5679 & 0.4302 & 0.3097 & **0.8198** & 0.7647 & 0.7391 & 0.6171 & 0 & 0.019 & 0.0044 & 0.0161 \\ \hline I, S & 0.659 & 0.6056 & 0.3004 & 0.3098 & 0.8112 & 0.7453 & 0.729 & 0.6274 & 0.0001 & 0.0475 & 0 & 0.0082 \\ \hline I, T & 0.6135 & 0.5577 & 0.4392 & 0.3105 & 0.7989 & 0.7311 & 0.7436 & 0.6377 & 0 & 0.0909 & 0.0001 & 0.0071 \\ \hline C & 0.6698 & **0.68** & 0.6295 & 0.6952 & 0.8134 & **0.7767** & 0.7708 & 0.8175 & 0 & **0.1119** & 0.0294 & 0.0022 \\ \hline C, I & **0.6808** & 0.6626 & 0.6323 & 0.6802 & 0.8183 & 0.7621 & 0.7277 & 0.8181 & 0 & 0.1094 & **0.0961** & 0.0002 \\ \hline C, S & 0.659 & 0.6653 & 0.6486 & 0.677 & 0.8083 & 0.76 & 0.7976 & 0.8058 & 0.0056 & 0.0287 & 0 & 0.0018 \\ \hline C, T & 0.6652 & 0.654 & 0.6055 & 0.6908 & 0.8084 & 0.7373 & 0.6681 & 0.8188 & 0 & 0.0648 & 0 & 0 \\ \hline S & 0.6582 & 0.6666 & 0.6474 & 0.6856 & 0.8102 & 0.7523 & 0.7225 & 0.8175 & 0.0022 & 0.008 & 0 & 0.0021 \\ \hline T & 0.6587 & 0.6396 & **0.6636** & **0.6964** & 0.8151 & 0.7479 & **0.8078** & **0.8262** & 0.0001 & 0.0502 & 0 & 0 \\ \hline R(B) & 0.6483 & 0.5266 & 0.5231 & 0.4158 & 0.7923 & 0.