Factuality challenges in the era of large language models and opportunities for fact-checking

IF 18.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Isabelle Augenstein, Timothy Baldwin, Meeyoung Cha, Tanmoy Chakraborty, Giovanni Luca Ciampaglia, David Corney, Renee DiResta, Emilio Ferrara, Scott Hale, Alon Halevy, Eduard Hovy, Heng Ji, Filippo Menczer, Ruben Miguez, Preslav Nakov, Dietram Scheufele, Shivam Sharma, Giovanni Zagni
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引用次数: 0

Abstract

The emergence of tools based on large language models (LLMs), such as OpenAI’s ChatGPT and Google’s Gemini, has garnered immense public attention owing to their advanced natural language generation capabilities. These remarkably natural-sounding tools have the potential to be highly useful for various tasks. However, they also tend to produce false, erroneous or misleading content—commonly referred to as hallucinations. Moreover, LLMs can be misused to generate convincing, yet false, content and profiles on a large scale, posing a substantial societal challenge by potentially deceiving users and spreading inaccurate information. This makes fact-checking increasingly important. Despite their issues with factual accuracy, LLMs have shown proficiency in various subtasks that support fact-checking, which is essential to ensure factually accurate responses. In light of these concerns, we explore issues related to factuality in LLMs and their impact on fact-checking. We identify key challenges, imminent threats and possible solutions to these factuality issues. We also thoroughly examine these challenges, existing solutions and potential prospects for fact-checking. By analysing the factuality constraints within LLMs and their impact on fact-checking, we aim to contribute to a path towards maintaining accuracy at a time of confluence of generative artificial intelligence and misinformation. Large language models (LLMs) present challenges, including a tendency to produce false or misleading content and the potential to create misinformation or disinformation. Augenstein and colleagues explore issues related to factuality in LLMs and their impact on fact-checking.
大型语言模型时代的事实挑战与事实核查机遇
基于大型语言模型(LLM)的工具,如 OpenAI 的 ChatGPT 和谷歌的 Gemini,因其先进的自然语言生成能力而备受公众关注。这些听起来非常自然的工具有可能在各种任务中发挥巨大作用。然而,它们也容易产生虚假、错误或误导性的内容--通常被称为幻觉。此外,LLM 还可能被滥用,大规模生成令人信服的虚假内容和简介,从而可能欺骗用户并传播不准确的信息,对社会构成巨大挑战。因此,事实核查变得越来越重要。尽管法律硕士在事实准确性方面存在问题,但他们在各种支持事实检查的子任务中表现出了熟练的能力,这对于确保回复的事实准确性至关重要。鉴于这些问题,我们探讨了与法律硕士的事实准确性有关的问题及其对事实核查的影响。我们确定了这些事实真实性问题所面临的主要挑战、迫在眉睫的威胁和可能的解决方案。我们还深入研究了这些挑战、现有解决方案以及事实核查的潜在前景。通过分析法律文献中的事实性限制及其对事实核查的影响,我们旨在为在生成式人工智能和错误信息交织的时代保持准确性的道路做出贡献。
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来源期刊
CiteScore
36.90
自引率
2.10%
发文量
127
期刊介绍: Nature Machine Intelligence is a distinguished publication that presents original research and reviews on various topics in machine learning, robotics, and AI. Our focus extends beyond these fields, exploring their profound impact on other scientific disciplines, as well as societal and industrial aspects. We recognize limitless possibilities wherein machine intelligence can augment human capabilities and knowledge in domains like scientific exploration, healthcare, medical diagnostics, and the creation of safe and sustainable cities, transportation, and agriculture. Simultaneously, we acknowledge the emergence of ethical, social, and legal concerns due to the rapid pace of advancements. To foster interdisciplinary discussions on these far-reaching implications, Nature Machine Intelligence serves as a platform for dialogue facilitated through Comments, News Features, News & Views articles, and Correspondence. Our goal is to encourage a comprehensive examination of these subjects. Similar to all Nature-branded journals, Nature Machine Intelligence operates under the guidance of a team of skilled editors. We adhere to a fair and rigorous peer-review process, ensuring high standards of copy-editing and production, swift publication, and editorial independence.
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