# Optimizing Large Language Models: A Deep Dive into Effective Prompt Engineering Techniques
## 5 Simulation Result
### TruthfulQA  
Tables 12 and 13 present the performance of Llama3, Gemma2, and Mistral on the TruthfulQA dataset, evaluated using BLEU, ROUGE, METEOR, BLEURT, and BERTScore metrics under different combinations of Prompt Engineering techniques. As observed in these tables, the TruthfulQA dataset focuses on assessing the truthfulness and cognitive biases of language models, where techniques like SSR, ToT, and CoT-based RAG generally yield strong performance.  
Furthermore, the optimal combination of these Prompt Engineering techniques varies depending on the LLM and the evaluation metric. For Llama3, the combination of SSR and ToT yields the best performance. Similarly, Gemma2 also performs best with the combination of SSR and ToT. In contrast, for Mistral, the combination of ToT and CoT-based RAG tends to deliver the highest performance.  
Fundamentally, as seen in Tables 12 and 13, Mistral generally exhibits the best performance when no Prompt Engineering techniques are applied, leading in four out of five evaluation metrics. This suggests that the foundational Mistral model has been pre-trained particularly well for the TruthfulQA dataset. On the other hand, Llama3, which initially exhibited the lowest performance across all five metrics, shows the most significant improvement when Prompt Engineering strategies are applied, ultimately outperforming all other models across all five metrics.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Llama3** & **Gemma2** & **Mistral** & **Method** & **Llama3** & **Gemma2** & **Mistral** \\ \hline \multicolumn{8}{c}{**BLEURT**} \\ \hline  
(B)** & 0.2907 & 0.3459 & 0.2568 & **S, T, R(B)** & 0.5825 & 0.6203 & 0.4884 \\ \hline
**S, T, R(C)** & 0.3927 & 0.4746 & 0.3929 & **S, T, R(C)** & 0.9402 & 0.9701 & 0.9462 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Performances of BLUERT and BERTScore according to LLMs and Prompt Engineering techniques on the MMLU dataset.  
Notably, despite Llama3's foundational PLM showing the poorest performance initially, the application of PE strategies enhanced its capabilities, making it the top-performing LLM. Mistral follows as the second-best performing LLM, while Gemma2 shows the least favorable results. Particularly noteworthy is the improvement observed in Mistral when applying techniques like CoT-based RAG, which significantly boosts its performance on the TruthfulQA dataset compared to the baseline PLM model.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **Llama3** & **Gemma2** & **Mistral** & **Method** & **Llama3** & **Gemma2** & **Mistral** \\ \hline \multicolumn{8}{c}{**BLEURT**} & \multicolumn{8}{c}{**BERTScore**} \\ \hline
**B** & 0.2011 & 0.2725 & 0.3078 & **B** & 0.3992 & 0.4850 & 0.4741 \\ \hline
**I** & 0.2227 & 0.2677 & 0.3378 & **I** & 0.4531 & 0.4906 & 0.4965 \\ \hline
**C** & 0.2290 & 0.2856 & 0.3455 & **C** & 0.4553 & 0.4864 & 0.5519 \\ \hline
**C**, **I** & 0.2519 & 0.3046 & 0.3397 & **C**, **I** & 0.4715 & 0.5175 & 0.5353 \\ \hline
**R(B)** & 0.2970 & 0.3156 & 0.2439 & **R(B)** & 0.4950 & 0.5214 & 0.3377 \\ \hline
**R(C)** & 0.3551 & 0.3538 & 0.3532 & **R(C)** & 0.5598 & 0.5416 & 0.5091 \\ \hline
**T**, **B** & 0.2130 & 0.2334 & 0.3233 & **T, B** & 0.3994 & 0.4463 & 0.5174 \\ \hline
**T**, **I** & 0.2563 & 0.2506 & 0.3545 & **T, I** & 0.4552 & 0.4505 & 0.5338 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Performances of BLUERT and BERTScore according to LLMs and Prompt Engineering techniques on the TruthfulQA 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
**B** & 3.2280 & 6.4854 & 7.9794 & **B** & 0.1162 & 0.1965 & 0.1966 & **B** & 0.0883 & 0.1490 & 0.2030 \\ \hline
**I** &