This paper investigates the inherent knowledge in language models from the perspective of epistemological holism. The purpose of this paper is to explore whether LLMs exhibit characteristics consistent with epistemological holism. These characteristics suggest that core knowledge, such as general scientific knowledge, each plays a specific role, serving as the foundation of our knowledge system and being difficult to revise. To assess these traits related to holism, we created a scientific reasoning dataset and examined the epistemology of language models through three tasks: Abduction, Revision, and Argument Generation. In the abduction task, the language models explained situations while avoiding revising the core knowledge. However, in other tasks, the language models were revealed not to distinguish between core and peripheral knowledge, showing an incomplete alignment with holistic knowledge principles.

翻译：本文从认识论整体论的视角探讨了语言模型中的内在知识。旨在探究大语言模型是否展现出与认识论整体论相一致的特征。这些特征表明，诸如通用科学知识等核心知识各自发挥着特定作用，构成我们知识体系的基础且难以修正。为评估这些与整体论相关的特质，我们构建了一个科学推理数据集，并通过三项任务——溯因推理、修正推理与论点生成——对语言模型的认识论进行检验。在溯因任务中，语言模型在避免修正核心知识的前提下对情境进行了解释。然而在其他任务中，模型并未区分核心知识与边缘知识，显示出与整体性知识原则的不完全一致性。