Summary of Attacks, Defenses and Evaluations For Llm Conversation Safety: a Survey, by Zhichen Dong et al.
Attacks, Defenses and Evaluations for LLM Conversation Safety: A Survey
by Zhichen Dong, Zhanhui Zhou, Chao Yang, Jing Shao, Yu Qiao
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper provides a comprehensive overview of recent studies on Large Language Models (LLMs) in conversation applications, specifically focusing on the risks and mitigation strategies for generating harmful responses. The authors categorize 20+ studies into three critical aspects: attacks, defenses, and evaluations. This survey aims to enhance understanding of LLM conversation safety and encourage further investigation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is important because it helps us understand how large language models can be used safely in conversations. Researchers have been working on ways to make sure these models don’t produce harmful responses, but there’s still much to learn. This paper brings together many recent studies on the topic, making it easier for experts and non-experts alike to understand the current state of research. |
