Attacks, Defenses and Evaluations for LLM Conversation Safety: A Survey
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916179131498496 |
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| author | Dong, Zhichen Zhou, Zhanhui Yang, Chao Shao, Jing Qiao, Yu |
| author_facet | Dong, Zhichen Zhou, Zhanhui Yang, Chao Shao, Jing Qiao, Yu |
| contents | Large Language Models (LLMs) are now commonplace in conversation applications. However, their risks of misuse for generating harmful responses have raised serious societal concerns and spurred recent research on LLM conversation safety. Therefore, in this survey, we provide a comprehensive overview of recent studies, covering three critical aspects of LLM conversation safety: attacks, defenses, and evaluations. Our goal is to provide a structured summary that enhances understanding of LLM conversation safety and encourages further investigation into this important subject. For easy reference, we have categorized all the studies mentioned in this survey according to our taxonomy, available at: https://github.com/niconi19/LLM-conversation-safety. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09283 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Attacks, Defenses and Evaluations for LLM Conversation Safety: A Survey Dong, Zhichen Zhou, Zhanhui Yang, Chao Shao, Jing Qiao, Yu Computation and Language Artificial Intelligence Computers and Society Machine Learning Large Language Models (LLMs) are now commonplace in conversation applications. However, their risks of misuse for generating harmful responses have raised serious societal concerns and spurred recent research on LLM conversation safety. Therefore, in this survey, we provide a comprehensive overview of recent studies, covering three critical aspects of LLM conversation safety: attacks, defenses, and evaluations. Our goal is to provide a structured summary that enhances understanding of LLM conversation safety and encourages further investigation into this important subject. For easy reference, we have categorized all the studies mentioned in this survey according to our taxonomy, available at: https://github.com/niconi19/LLM-conversation-safety. |
| title | Attacks, Defenses and Evaluations for LLM Conversation Safety: A Survey |
| topic | Computation and Language Artificial Intelligence Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2402.09283 |