Prompt Engineering has gained significant relevance in recent years, fueled by advancements in pre-trained and large language models. However, a critical issue has been identified within this domain: the lack of sensitivity and robustness of these models towards Prompt Templates, particularly in lesser-studied languages such as Japanese. This paper explores this issue through a comprehensive evaluation of several representative Large Language Models (LLMs) and a widely-utilized pre-trained model(PLM), T5. These models are scrutinized using a benchmark dataset in Japanese, with the aim to assess and analyze the performance of the current multilingual models in this context. Our experimental results reveal startling discrepancies. A simple modification in the sentence structure of the Prompt Template led to a drastic drop in the accuracy of GPT-4 from 49.21 to 25.44. This observation underscores the fact that even the highly performance GPT-4 model encounters significant stability issues when dealing with diverse Japanese prompt templates, rendering the consistency of the model's output results questionable. In light of these findings, we conclude by proposing potential research trajectories to further enhance the development and performance of Large Language Models in their current stage.

翻译：提示工程近年来因预训练模型和大型语言模型的进步而备受关注。然而，该领域存在一个关键问题：这些模型对提示模板缺乏敏感性和鲁棒性，尤其在日语等研究较少的语言中表现突出。本文通过对多个代表性大型语言模型及广泛使用的预训练模型T5进行综合评估，探究这一问题。我们使用日语基准数据集对这些模型进行检验，旨在评估并分析当前多语言模型在此背景下的性能表现。实验结果显示惊人差异：仅对提示模板的句子结构进行简单修改，就导致GPT-4的准确率从49.21骤降至25.44。这一观察结果表明，即使是性能卓越的GPT-4模型，在处理多样的日语提示模板时也面临显著稳定性问题，使得模型输出结果的一致性存疑。基于这些发现，我们最后提出了可能的研究方向，以进一步推动当前阶段大型语言模型的开发与性能提升。