# Optimizing Large Language Models: A Deep Dive into Effective Prompt Engineering Techniques
## 5 Simulation Result
### Overall Performance Analysis and PE Strategies  
Table 16 summarizes the BLEU, ROUGE, METEOR, BLEURT, and BERTScore performance metrics for the three LLMs--Llama3, Gemma2, and Mistral--based on the PE strategy employed, as presented in Tables 16 through Tables 4-15. This table also identifies the optimal PE strategy for each dataset and the best-performing LLM per dataset. As demonstrated in Table 16, it becomes apparent that there is an optimal Prompt Engineering technique that can be universally applied across all LLMs, contingent upon the characteristics of the dataset. For instance, datasets such as ARC, GSM8k, HellaSwag, MMLU, and Winograde, which demand advanced reasoning abilities in diverse specialized domains including mathematics, science, and humanities, tend to benefit from Prompt Engineering strategies rooted in CoT or a combination of CoT and ICL. In contrast, for datasets like TruthfulQA, where the primary objectives are information retrieval and truthfulness, RAG-based approaches exhibit superior performance. This distinction underscores that the preferred Prompt Engineering technique varies in alignment with the specific objectives of each dataset. Nevertheless, it is evident that the optimal PE strategy is not uniform across all models and datasets, but rather varies depending on the specific LLM and dataset in question. For example, in the ARC dataset, which demands mathematical reasoning skills, the most effective PE strategy differs among Llama3, Gemma2, and Mistral. Although all approaches involve CoT or build upon it, the ideal PE combinations vary by model, such as CoT for Llama3, SSR combined with CoT and ICL for Gemma2, and a combination of SSR, ToT, CoT, and ICL for Mistral. In the absence of a Prompt Engineering strategy, the performance of the base PLM varies depending on the dataset. However, Gemma2 generally demonstrates superior performance, especially on datasets that involve multiple-choice questions related to scientific topics, language understanding and generation, and contextual reasoning in commonsense content (e.g., ARC, MMLU, Winogrande). Llama3 excels on datasets requiring natural language understanding and related reasoning capabilities, such as GSM8K and HellaSwag, while Mistral shows its strengths in datasets focused on truthfulness and cognitive error detection, such as TruthfulQA. When PE strategies are employed, the intrinsic characteristics of the LLMs become particularly influential, with results generally aligning with the trends observed in the selection of the most suitable PLM for each dataset. Specifically, as demonstrated in Table 2, language models that have undergone more advanced pre- and post-training techniques (e.g., Gemma2) exhibit a heightened capacity to either amplify their inherent strengths or mitigate their weaknesses when PE strategies are implemented. This is exemplified by Gemma2's outstanding performance on the HellaSwag dataset, where its advantages are further emphasized, while Llama3 attains the highest performance on the TruthfulQA dataset. When considering datasets that encompass a mix of diverse categories, i.e., essentially reflecting more general datasets, aggregating the occurrences of the most effective Prompt Engineering techniques as presented in Table 16 can provide valuable insights into which methods are likely to be most effective across a broad range of datasets. Table 17 summarizes the cumulative usage of various Prompt Engineering techniques across different datasets. The experimental results reveal that the CoT technique was the most frequently employed, appearing 56 times. The Tree of Thought method followed with 47 instances, while ICL and SSR were each utilized 35 times. Although the R(C) technique was used 28 times and showed strong performance on specific datasets, it did not exhibit the same level of consistent performance as the CoT method. These findings suggest that the CoT technique is particularly effective in enhancing problem-solving and reasoning capabilities across a wide variety of datasets.