PromptBench: Towards Evaluating the Robustness of Large Language Models
  on Adversarial Prompts
Authors: name: Kaijie Zhu, name: Jindong Wang, name: Jiaheng Zhou, name: Zichen Wang, name: Hao Chen, name: Yidong Wang, name: Linyi Yang, name: Wei Ye, name: Neil Zhenqiang Gong, name: Yue Zhang, name: Xing Xie

The increasing reliance on Large Language Models (LLMs) across academia and
industry necessitates a comprehensive understanding of their robustness to
prompts. In response to this vital need, we introduce PromptBench, a robustness
benchmark designed to measure LLMs' resilience to adversarial prompts. This
study uses a plethora of adversarial textual attacks targeting prompts across
multiple levels: character, word, sentence, and semantic. These prompts are
then employed in diverse tasks, such as sentiment analysis, natural language
inference, reading comprehension, machine translation, and math
problem-solving. Our study generates 4,032 adversarial prompts, meticulously
evaluated over 8 tasks and 13 datasets, with 567,084 test samples in total. Our
findings demonstrate that contemporary LLMs are vulnerable to adversarial
prompts. Furthermore, we present comprehensive analysis to understand the
mystery behind prompt robustness and its transferability. We then offer
insightful robustness analysis and pragmatic recommendations for prompt
composition, beneficial to both researchers and everyday users. We make our
code, prompts, and methodologies to generate adversarial prompts publicly
accessible, thereby enabling and encouraging collaborative exploration in this
pivotal field: https://github.com/microsoft/promptbench.