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.

翻译：基于大语言模型（LLMs）的工具（如OpenAI的ChatGPT、微软的Bing Chat和谷歌的Bard）的出现引起了公众的广泛关注。这些极具实用性且听起来自然的工具标志着自然语言生成的重大进步，但它们也表现出生成虚假、错误或误导性内容的倾向——通常被称为“幻觉”。此外，LLMs可能被用于恶意目的，例如大规模生成虚假但听起来可信的内容和档案。这对社会构成了重大挑战，涉及用户可能被欺骗以及不准确信息的日益传播。鉴于这些风险，我们探讨了事实核查人员、新闻机构以及更广泛的研究和政策界所需的技术创新、监管改革和人工智能素养倡议。通过识别风险、迫在眉睫的威胁以及一些可行的解决方案，我们旨在阐明如何在生成式AI时代驾驭真实性的各个方面。