PromptBench: Towards Evaluating the Robustness of Large Language Models
  on Adversarial Prompts
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.
64813227c0d3c70316d56be3
Kaijie Zhu
claimed_verified
2024-05-06T07:06:01.611Z
641fb3733c49fc33cedb4429
/avatars/3577bca726282ed4b26009f3a2d5e87d.svg
Kaijie Zhu
user
March07
64813227c0d3c70316d56be4
Jindong Wang
claimed_verified
2023-06-12T09:12:46.552Z
6204cc0d522e40b4a18d86e2
/avatars/18daf2de5671e711dc745388dd60569d.svg
Jindong Wang
user
jindongwang
64813227c0d3c70316d56be5
Jiaheng Zhou
64813227c0d3c70316d56be6
Zichen Wang
64813227c0d3c70316d56be7
Hao Chen
64813227c0d3c70316d56be8
Yidong Wang
64813227c0d3c70316d56be9
Linyi Yang
claimed_verified
2023-12-29T08:59:04.552Z
64895683f534abe18eec264b
/avatars/73cc9e6db6db86793787750776b57c63.svg
Linyi Yang
user
linyiyang2023
64813227c0d3c70316d56bea
Wei Ye
64813227c0d3c70316d56beb
Neil Zhenqiang Gong
64813227c0d3c70316d56bec
Yue Zhang
64813227c0d3c70316d56bed
Xing Xie
admin_assigned
2023-06-12T13:55:08.886Z
64785ca82b3730b6b63b75a2
/avatars/b0c4f7fb4d4c556118fa6b52d3615429.svg
Xing Xie
user
xingustc
PromptBench: Towards Evaluating the Robustness of Large Language Models
  on Adversarial Prompts
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.
PromptBench evaluates large language models' robustness to adversarial prompts across various tasks and datasets, revealing vulnerabilities and providing recommendations for prompt composition.
Large Language Models LLMs PromptBench adversarial prompts sentiment analysis natural language inference reading comprehension machine translation math problem-solving