Risk Assessment of Large Language Models Beyond Apocalyptic Visions

Abstract

The remarkable development of Large Language Models (LLMs) continues to revolutionize various human activities in different societal domains like education, communications, and healthcare. While facilitating the generation of coherent and contextually relevant text across a diverse plethora of topics, LLMs became a set of instruments available in different toolboxes of decision makers. In this way, LLMs moved from a hype to an actual underlying mechanism for capturing valuable insights, revealing different perspectives on topics, and providing real-time decision-making support. As LLMs continue to increase in sophistication and accessibility, both societal and academic effort from AI and cyber security is projected in this direction, and a general societal unrest is seen due to their unknown consequences. Nevertheless, an apocalyptic vision towards their risks and impact does not represent a constructive and realistic approach. Contrarily, this could be an impediment to building LLMs that are safe, responsible, trustworthy, and have a real contribution to the overall societal well-being. Hence, understanding and addressing the risks of LLMs is imperative for building them in an ethical, social, and legal manner while making sure to consider control mechanisms for avoiding, mitigating, accepting, and transferring their risks and harmful consequences. Taking into consideration that these technological developments find themselves in an incipient phase, this research calls for a multi-angled perspective and proposes a realistic theoretical risk assessment method for LLMs.