Summary of Risk Taxonomy, Mitigation, and Assessment Benchmarks Of Large Language Model Systems, by Tianyu Cui et al.
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems
by Tianyu Cui, Yanling Wang, Chuanpu Fu, Yong Xiao, Sijia Li, Xinhao Deng, Yunpeng Liu, Qinglin Zhang, Ziyi Qiu, Peiyang Li, Zhixing Tan, Junwu Xiong, Xinyu Kong, Zujie Wen, Ke Xu, Qi Li
First submitted to arxiv on: 11 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent surge in advancements in large language models (LLMs) has led to concerns over safety and security issues hindering their widespread adoption. Despite numerous studies on mitigation strategies, there is an urgent need for a comprehensive framework to categorize existing research. This paper delves into the four core components of LLM systems: input module, language model, toolchain module, and output module. By analyzing potential risks and corresponding mitigation strategies for each module, this taxonomy aims to provide a systematic perspective on building responsible LLM systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can solve many natural language processing tasks, but they also pose safety and security concerns that need to be addressed. This paper helps us understand how these models work and what we can do to make them safer. |
Keywords
* Artificial intelligence * Language model * Natural language processing
