title: PromptBench: Towards Evaluating the Robustness of Large Language Models
    on Adversarial Prompts
summary: 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.
paper_authors._id: 64813227c0d3c70316d56be3
paper_authors.name: Kaijie Zhu
paper_authors.status: claimed_verified
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paper_authors.fullname: Kaijie Zhu
paper_authors.type: user
paper_authors.user: March07
paper_authors._id: 64813227c0d3c70316d56be4
paper_authors.name: Jindong Wang
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paper_authors.fullname: Jindong Wang
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paper_authors.user: jindongwang
paper_authors._id: 64813227c0d3c70316d56be5
paper_authors.name: Jiaheng Zhou
paper_authors._id: 64813227c0d3c70316d56be6
paper_authors.name: Zichen Wang
paper_authors._id: 64813227c0d3c70316d56be7
paper_authors.name: Hao Chen
paper_authors._id: 64813227c0d3c70316d56be8
paper_authors.name: Yidong Wang
paper_authors._id: 64813227c0d3c70316d56be9
paper_authors.name: Linyi Yang
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paper_authors.fullname: Linyi Yang
paper_authors.type: user
paper_authors.user: linyiyang2023
paper_authors._id: 64813227c0d3c70316d56bea
paper_authors.name: Wei Ye
paper_authors._id: 64813227c0d3c70316d56beb
paper_authors.name: Neil Zhenqiang Gong
paper_authors._id: 64813227c0d3c70316d56bec
paper_authors.name: Yue Zhang
paper_authors._id: 64813227c0d3c70316d56bed
paper_authors.name: Xing Xie
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-12T13:55:08.886Z
paper_authors._id: 64785ca82b3730b6b63b75a2
paper_authors.avatarUrl: /avatars/b0c4f7fb4d4c556118fa6b52d3615429.svg
paper_authors.fullname: Xing Xie
paper_authors.type: user
paper_authors.user: xingustc
paper_title: PromptBench: Towards Evaluating the Robustness of Large Language Models
    on Adversarial Prompts
paper_summary: 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.
paper_ai_summary: PromptBench evaluates large language models' robustness to adversarial prompts across various tasks and datasets, revealing vulnerabilities and providing recommendations for prompt composition.
paper_ai_keywords: Large Language Models LLMs PromptBench adversarial prompts sentiment analysis natural language inference reading comprehension machine translation math problem-solving