Factuality Challenges in the Era of Large Language Models
This tackles the societal challenge of deception and misinformation spread by LLMs, but it is incremental as it synthesizes existing concerns without introducing new methods.
The paper addresses the problem of large language models (LLMs) generating false or misleading content, known as hallucinations, and their potential for malicious use, proposing technological, regulatory, and educational solutions to mitigate these risks.
The emergence of tools based on Large Language Models (LLMs), such as OpenAI's ChatGPT, Microsoft's Bing Chat, and Google's Bard, has garnered immense public attention. These incredibly useful, natural-sounding tools mark significant advances in natural language generation, yet they exhibit a propensity to generate false, erroneous, or misleading content -- commonly referred to as "hallucinations." Moreover, LLMs can be exploited for malicious applications, such as generating false but credible-sounding content and profiles at scale. This poses a significant challenge to society in terms of the potential deception of users and the increasing dissemination of inaccurate information. In light of these risks, we explore the kinds of technological innovations, regulatory reforms, and AI literacy initiatives needed from fact-checkers, news organizations, and the broader research and policy communities. By identifying the risks, the imminent threats, and some viable solutions, we seek to shed light on navigating various aspects of veracity in the era of generative AI.