The Dark Side of Language Models: Exploring the Potential of LLMs in Multimedia Disinformation Generation and Dissemination

Abstract

Disinformation - the deliberate spread of false or misleading information poses a significant threat to our society by undermining trust, exacerbating polarization, and manipulating public opinion. With the rapid advancement of artificial intelligence and the growing prominence of large language models (LLMs) such as ChatGPT, new avenues for the dissemination of disinformation are emerging. This review paper explores the potential of LLMs to initiate the generation of multi-media disinformation, encompassing text, images, audio, and video. We begin by examining the capabilities of LLMs, highlighting their potential to create compelling, context-aware content that can be weaponized for malicious purposes. Subsequently, we examine the nature of disinformation and the various mechanisms through which it spreads in the digital landscape. Utilizing these advanced models, malicious actors can automate and scale up disinformation effectively. We describe a theoretical pipeline for creating and disseminating disinformation on social media. Existing interventions to combat disinformation are also reviewed. While these efforts have shown success, we argue that they need to be strengthened to effectively counter the escalating threat posed by LLMs. Digital platforms have, unfortunately, enabled malicious actors to extend the reach of disinformation. The advent of LLMs poses an additional concern as they can be harnessed to significantly amplify the velocity, variety, and volume of disinformation. Thus, this review proposes augmenting current interventions with AI tools like LLMs, capable of assessing information more swiftly and comprehensively than human fact-checkers. This paper illuminates the dark side of LLMs and highlights their potential to be exploited as disinformation dissemination tools.