  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 PromptRobust, 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. The
adversarial prompts, crafted to mimic plausible user errors like typos or
synonyms, aim to evaluate how slight deviations can affect LLM outcomes while
maintaining semantic integrity. These prompts are then employed in diverse
tasks including sentiment analysis, natural language inference, reading
comprehension, machine translation, and math problem-solving. Our study
generates 4,788 adversarial prompts, meticulously evaluated over 8 tasks and 13
datasets. Our findings demonstrate that contemporary LLMs are not robust to
adversarial prompts. Furthermore, we present a 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.
