AI Safety Landscape for Large Language Models: Taxonomy, State-of-the-art, and Future Directions
Chen Chen, Xueluan Gong, Ziyao Liu, Weifeng Jiang, Si Qi Goh, Kwok-Yan Lam
Why It Matters
What makes this one worth your time
Understanding AI Safety is crucial for the responsible deployment of AI systems, especially as they become more integrated into critical areas affecting public safety and national security.
A comprehensive framework and review of AI Safety in the context of Large Language Models.
Summary
The paper proposes a novel architectural framework for AI Safety, categorizing it into Trustworthy AI, Responsible AI, and Safe AI, and reviews current research and advancements in these areas, focusing on Large Language Models.
Key contributions
- Proposes a novel architectural framework for AI Safety.
- Provides an extensive review of current research in AI Safety.
- Highlights key challenges and mitigation approaches in AI Safety for Large Language Models.
Notable insights
- The paper categorizes AI Safety into three distinct perspectives: Trustworthy AI, Responsible AI, and Safe AI.
- It highlights innovative mechanisms and methodologies for designing and testing AI safety in Large Language Models.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2408.12935v4 Announce Type: replace Abstract: AI Safety is an emerging area of critical importance to the safe adoption and deployment of AI systems. With the rapid proliferation of AI and especially with the recent advancement of Generative AI (or GAI), the technology ecosystem behind the design, development, adoption, and deployment of AI systems has drastically changed, broadening the scope of AI Safety to address impacts on public safety and national security. In this paper, we propose a novel architectural framework for understanding and analyzing AI Safety; defining its characteristics from three perspectives: Trustworthy AI, Responsible AI, and Safe AI. We provide an extensive review of current research and advancements in AI safety from these perspectives, highlighting their key challenges and mitigation approaches. Through examples from state-of-the-art technologies, particularly Large Language Models (LLMs), we present innovative mechanism, methodologies, and techniques for designing and testing AI safety. Our goal is to promote advancement in AI safety research, and ultimately enhance people's trust in digital transformation.