Most large language models (LLMs) are sensitive to prompts, and another
synonymous expression or a typo may lead to unexpected results for the model.
Composing an optimal prompt for a specific demand lacks theoretical support and
relies entirely on human experimentation, which poses a considerable obstacle
to popularizing generative artificial intelligence. However, there is no
systematic analysis of the stability of LLMs in resisting prompt perturbations
in real-world scenarios. In this work, we propose to evaluate the ease-of-use
of LLMs and construct E-Bench, simulating the actual situation of human use
from synonymous perturbation (including paraphrasing, simplification, and
colloquialism) and typographical perturbation (such as typing). On this basis,
we also discuss the combination of these two types of perturbation and analyze
the main reasons for performance degradation. Experimental results indicate
that with the increase of model size, although the ease-of-use are
significantly improved, there is still a long way to go to build a sufficiently
user-friendly model.

通过系统分析大型语言模型（LLMs）抵抗提示扰动的稳定性，本研究构建 E-Bench 模拟真实人类使用情况，并讨论了同义扰动和排版扰动的组合以及性能下降的主要原因，实验证明即便随着模型规模的增加，易用性显著提升，但仍需努力建立足够用户友好的模型。