# Optimizing Large Language Models: A Deep Dive into Effective Prompt Engineering Techniques
## 5 Simulation Result
### HellaSwag
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Method** & **Llama3** & **Gemma2** & **Mistral** & **Method** & **Llama3** & **Gemma2** & **Mistral** & **Method** & **Llama3** & **Gemma2** & **Mistral** \\ \hline \multicolumn{8}{c}{**BOUGE**} \\ \hline
**B** & 0.9938 & 0.4228 & 0.8254 & **B** & 0.1477 & 0.1335 & 0.1463 & **B** & 0.0951 & 0.0926 & 0.1034 \\ \hline
**I** & 0.1473 & 0.3997 & 0.2550 & **I** & 0.0855 & 0.1429 & 0.0942 & **I** & 0.0469 & 0.1047 & 0.0659 \\ \hline
**C** & 0.0193 & 0.0000 & 0.0531 & **C** & 0.3168 & 0.6521 & 0.2600 & **C** & **0.1592** & 0.3265 & 0.1312 \\ \hline
**C, I** & **1.8112** & 0.0000 & 0.0000 & **C, I** & 0.3604 & 0.7168 & **0.3206** & **C, I** & 0.1807 & 0.3586 & 0.1605 \\ \hline
**R(B)** & 0.8526 & 0.7209 & 0.6942 & **R(B)** & 0.1309 & 0.1488 & 0.1205 & **R(B)** & 0.0822 & 0.0999 & 0.0858 \\ \hline
**R(C)** & 1.0346 & 0.0000 & **2.0251** & **R(C)** & 0.3440 & 0.7158 & 0.2680 & **R(C)** & 0.1715 & **0.3584** & 0.1344 \\ \hline
**T, B** & 0.8721 & 0.2981 & 0.9997 & **T, B** & 0.1434 & 0.1290 & 0.1636 & **T, B** & 0.0999 & 0.0897 & 0.1222 \\ \hline
**T, I** & 1.0498 & 0.2970 & 0.7208 & **T, I** & 0.1399 & 0.1213 & 0.1335 & **T, I** & 0.1056 & 0.0854 & 0.0996 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performances of BLUE, ROUGE, METEOR according to LLMs and Prompt Engineering techniques on the HellaSwag dataset. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Method** & **Llama3** & **Gemma2** & **Mistral** & **Method** & **Llama3** & **Gemma2** & **Mistral** & **Method** & **Llama3** & **Gemma2** & **Mistral** \\ \hline \multicolumn{8}{c}{**BLEU**} & \multicolumn{8}{c}{**ROUGE**} & \multicolumn{8}{c}{**METEOR**} \\ \hline
**T, C** & 0.7567 & **2.1914** & 0.0888 & **T, C** & **0.4604** & 0.6989 & 0.3061 & **T, C** & 0.2305 & 0.3497 & 0.1535 \\ \hline
**T, C, I** & 0.0000 & 0.0000 & 0.0000 & **T, C, I** & 0.2633 & 0.7193 & 0.2937 & **T, C, I** & 0.1319 & 0.3599 & 0.1468 \\ \hline
**T, R(B)** & 0.7593 & 0.4324 & 1.1741 & **T, R(B)** & 0.1382 & 0.1373 & 0.1571 & **T, R(B)** & 0.0885 & 0.0917 & 0.1157 \\ \hline
**T, R(C)** & 0.0000 & 0.0000 & 0.7358 & **T, R(C)** & 0.3892 & **0.7382** & 0.2429 & **T, R(C)** & 0.1944 & 0.3693 & 0.1215 \\ \hline
**S, B** & 1.0413 & 0.4181 & 0.4664 & **S, B** & 0.1522 & 0.1345 & 0.1035 & **S, B** & 0.0982 & 0.0911 & 0.0705 \\ \hline
**S, I** & 0.0003 & 0.5170 & 0.2350 & **S, I** & 0.0204 & 0.1202 & 0.1059 & **S, I** & 0.0135 & 0.0842 & 0.0823 \\ \hline
**S, C** & 0.0054 & 1.5551 & 0.0240 & **S, C** & 0.1226 & 0.6381 & 0.3175 & **S, C** & 0.0641 & 0.3193 & **0.1614** \\ \hline
**S, C, I** & 0.0000 & 0.0000 & 0.5114 & **S, C, I** & 0.3116 & 0.6565 & 0.2992 & **S, C, I** & 0.1563 & 0.3283 & 0.1503 \\ \hline
**S, R(B)** & 0.7829 & 0.6365 & 1.3147 & **S, R(B)** & 0.1305 & 0.1395 & 0.1745 & **S, R(B)** & 0.0826 & 0.0952 & 0.1327 \\ \hline
**S, R(C)** & 0.5530 & 0.0000 & 0.9046 & **S, R(C)** & 0.3041 & 0.6775 & 0.3204 & **S, R(C)** & 0.1518 & 0.3387 & 0.1604 \\ \hline
**S, T, B** & 0.9205 & 0.3513 & 1.2806 & **S, T, B** & 0.1500 & 0.1355 & 0.1666 & **S, T, B** & 0.0979 & 0.0919 & 0.1271 \\ \hline
**S, T, I** & 1.1919 & 0.5674 & 1.1760 & **S, T, I** & 0.1496 & 0.1388 & 0.1510 & **S, T, I** & 0.1091 & 0.0971 & 0.1250 \\ \hline
**S, T, C** & 0.7736 & 0.9067 & 0.0124 & **S, T, C** & 0.4251 & 0.6858 & 0.2467 & **S, T, C** & 0.2125 & 0.3295 & 0.1252 \\ \hline
**S, T, C, I** & 0.0000 & 0.0000 & 0.0898 & **S, T, C, I** & 0.3718 & 0.7222 & 0.2683 & **S, T, C** & 1.0862 & 0.3614 & 0.1346 \\ \hline
**S, T, R(B)** & 0.9430 & 0.5889 & 1.3780 & **S, T, R(B)** & 0.1513 & 0.1473 & 0.1792 & **S, T, R(B)** & 0.0981 & 0.0993 & 0.1415 \\ \hline
**S, T, R(C)** & 0.0000 & 0.0000 & 0.3419 & **S, T, R(C)** & 0.3454 & 0.7133 & 0.2687 & **S, T, R(C)** & 0.1727 & 0.3569 & 0.1348 \\ \hline \hline \end{tabular}
\end{table}