Epistemology of Language Models: Do Language Models Have Holistic Knowledge?
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 19 of its 40 references that are in the map. First posted on arXiv 19 Mar 2024.
Builds on
19 of 40 references.
References · 19
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7 more
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Built on by
Citing papers per year: 2024: 4; 2025: 2; 2026: 3.
9 papers.
- 2024Know Your Limits: A Survey of Abstention in Large Language Modelscited by 116
- 2024The Art of Saying No: Contextual Noncompliance in Language Modelscited by 100
- 2025Sacred or Synthetic? Evaluating LLM Reliability and Abstention for Religious Questionscited by 10
- 2024ProgressGym: Alignment with a Millennium of Moral Progresscited by 9
- 2024When to Speak, When to Abstain: Contrastive Decoding with Abstentioncited by 7
- 2026Wiring the 'Why': A Unified Taxonomy and Survey of Abductive Reasoning in LLMscited by 5
- 2026Do LLMs have core beliefs?cited by 1
- 2025How do datasets, developers, and models affect biases in a low-resourced language?: The Case of the Bengali Languagecited by 0
- 2026Task Abstention for Large Language Models in Code Generationcited by 0
- 2026Task Abstention for Large Language Models in Code Generation16 May 2026
- 2026Do LLMs have core beliefs?5 May 2026
- 2026Wiring the 'Why': A Unified Taxonomy and Survey of Abductive Reasoning in LLMs9 Apr 2026
- 2025Sacred or Synthetic? Evaluating LLM Reliability and Abstention for Religious Questions4 Aug 2025
- 2025How do datasets, developers, and models affect biases in a low-resourced language?: The Case of the Bengali Language7 Jun 2025
- 2024When to Speak, When to Abstain: Contrastive Decoding with Abstention17 Dec 2024
- 2024Know Your Limits: A Survey of Abstention in Large Language Models25 Jul 2024
- 2024The Art of Saying No: Contextual Noncompliance in Language Models2 Jul 2024
- 2024ProgressGym: Alignment with a Millennium of Moral Progress28 Jun 2024
Similar, not cited
Close in wording, no citation either way
- 2025What Kind of Reasoning (if any) is an LLM actually doing? On the Stochastic Nature and Abductive Appearance of Large Language Models
- 2025What and Whose Knowledge? Measuring Epistemic Diversity in Large Language Models
- 2026On Epistemic Diversity in Large Language Models
- 2024Comparing Inferential Strategies of Humans and Large Language Models in Deductive Reasoning
- 2025LLMs, Reasoning and Plagiarism
- 2024Do Large Language Models Advocate for Inferentialism?
- 2026Fundamental Reasoning Paradigms Induce Out-of-Domain Generalization in Language Models
- 2024Reasoning Abilities of Large Language Models: In-Depth Analysis on the Abstraction and Reasoning Corpus