As AI technologies are rolled out into healthcare, academia, human resources, law, and a multitude of other domains, they become de-facto arbiters of truth. But truth is highly contested, with many different definitions and approaches. This article discusses the struggle for truth in AI systems and the general responses to date. It then investigates the production of truth in InstructGPT, a large language model, highlighting how data harvesting, model architectures, and social feedback mechanisms weave together disparate understandings of veracity. It conceptualizes this performance as an operationalization of truth, where distinct, often conflicting claims are smoothly synthesized and confidently presented into truth-statements. We argue that these same logics and inconsistencies play out in Instruct's successor, ChatGPT, reiterating truth as a non-trivial problem. We suggest that enriching sociality and thickening "reality" are two promising vectors for enhancing the truth-evaluating capacities of future language models. We conclude, however, by stepping back to consider AI truth-telling as a social practice: what kind of "truth" do we as listeners desire?

翻译：随着AI技术被部署到医疗、学术、人力资源、法律及众多其他领域，它们实际上已成为真相的仲裁者。但真相极具争议性，存在多种不同定义和方法。本文探讨了AI系统中真相的争夺战及迄今常见的应对措施，进而研究了大型语言模型InstructGPT中真相的生产机制，揭示了数据采集、模型架构和社会反馈机制如何将不同的真实性理解交织融合。本文将这一过程概念化为真相的操作化实现——其中不同甚至相互矛盾的主张被平滑综合，并以真相陈述的形式自信呈现。我们认为这些逻辑及其内在矛盾同样延续至InstructGPT的继任者ChatGPT，凸显真相作为一个非平凡问题。我们提出增强社会性和加厚"现实"是提升未来语言模型真相评估能力的两个有效方向。最后我们退一步反思：将AI的真相陈述视为社会实践——作为聆听者，我们究竟渴望何种"真相"？