Summary of Large Language Model Safety: a Holistic Survey, by Dan Shi et al.
Large Language Model Safety: A Holistic Survey
by Dan Shi, Tianhao Shen, Yufei Huang, Zhigen Li, Yongqi Leng, Renren Jin, Chuang Liu, Xinwei Wu, Zishan Guo, Linhao Yu, Ling Shi, Bojian Jiang, Deyi Xiong
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 The rapid development and deployment of large language models (LLMs) have introduced a new frontier in artificial intelligence, marked by unprecedented capabilities in natural language understanding and generation. To address the increasing integration of these models into critical applications, this paper investigates the potential risks and proposes associated mitigation strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores the risks and solutions for using large language models in important areas like AI. It looks at how these models work and what could go wrong if they’re used in things that matter a lot. |
Keywords
» Artificial intelligence » Language understanding
