mlsanityData to 7 Oct 2026

Do LLMs Dream of Ontologies?

arXiv 2401.14931 ↗First posted 26 Jan 2024cs.CL cs.AIcited by 3

Not on the map. Why?

Abstract (arXiv)

Large Language Models (LLMs) have demonstrated remarkable performance across diverse natural language processing tasks, yet their ability to memorize structured knowledge remains underexplored. In this paper, we investigate the extent to which general-purpose pre-trained LLMs retain and correctly reproduce concept identifier (ID)-label associations from publicly available ontologies. We conduct a systematic evaluation across multiple ontological resources, including the Gene Ontology, Uberon, Wikidata, and ICD-10, using LLMs such as Pythia-12B, Gemini-1.5-Flash, GPT-3.5, and GPT-4. Our findings reveal that only a small fraction of ontological concepts is accurately memorized, with GPT-4 demonstrating the highest performance. To understand why certain concepts are memorized more effectively than others, we analyze the relationship between memorization accuracy and concept popularity on the Web. Our results indicate a strong correlation between the frequency of a concept's occurrence online and the likelihood of accurately retrieving its ID from the label. This suggests that LLMs primarily acquire such knowledge through indirect textual exposure rather than directly from structured ontological resources. Furthermore, we introduce new metrics to quantify prediction invariance, demonstrating that the stability of model responses across variations in prompt language and temperature settings can serve as a proxy for estimating memorization robustness.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 30 of its 53 references that are in the map. First posted on arXiv 26 Jan 2024.

Builds on

30 of 53 references.

References · 30

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18 more
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Built on by

Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025OntoURL: A Benchmark for Evaluating Large Language Models on Symbolic Ontological Understanding, Reasoning and Learningcited by 6
  2. 2025From Memorization to Generalization: Fine-Tuning Large Language Models for Biomedical Term-to-Identifier Normalizationcited by 1
  3. 2025Remembering Unequally: Global and Disciplinary Bias in LLM Reconstruction of Scholarly Coauthor Listscited by 0