PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts
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arXiv
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866911956153139200 |
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| author | Zhu, Kaijie Wang, Jindong Zhou, Jiaheng Wang, Zichen Chen, Hao Wang, Yidong Yang, Linyi Ye, Wei Zhang, Yue Gong, Neil Zhenqiang Xie, Xing |
| author_facet | Zhu, Kaijie Wang, Jindong Zhou, Jiaheng Wang, Zichen Chen, Hao Wang, Yidong Yang, Linyi Ye, Wei Zhang, Yue Gong, Neil Zhenqiang Xie, Xing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_04528 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts Zhu, Kaijie Wang, Jindong Zhou, Jiaheng Wang, Zichen Chen, Hao Wang, Yidong Yang, Linyi Ye, Wei Zhang, Yue Gong, Neil Zhenqiang Xie, Xing Computation and Language Cryptography and Security Machine Learning 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. |
| title | PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts |
| topic | Computation and Language Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2306.04528 |