Foundations of LLM Knowledge Materialization: Termination, Reproducibility, Robustness

2025-10-08

AI Summary

This study addresses the foundational problem of structured extraction of implicit factual knowledge from large language models (LLMs). To overcome theoretical gaps in termination, reproducibility, and robustness exhibited by existing methods (e.g., GPTKB), we propose miniGPTKB—a lightweight, systematic framework. We conduct four-dimensional perturbation experiments—across seed, language, stochasticity (temperature), and model variants—across historical, entertainment, and financial domains. Evaluation employs three quantitative metrics: output yield, lexical similarity, and semantic similarity. Our results reveal, for the first time, that knowledge extraction achieves high termination rates and strong robustness across seeds and temperature settings, yet remains highly sensitive to language switching and model substitution. Crucially, core factual knowledge consistently emerges, but its reliability is bounded by model capability and linguistic consistency. These findings establish a verifiable methodology and empirical benchmark for grounding and operationalizing LLM-internal knowledge.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Natural Language Processing: Information Extraction
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Large Language Models (LLMs) encode substantial factual knowledge, yet measuring and systematizing this knowledge remains challenging. Converting it into structured format, for example through recursive extraction approaches such as the GPTKB methodology (Hu et al., 2025b), is still underexplored. Key open questions include whether such extraction can terminate, whether its outputs are reproducible, and how robust they are to variations. We systematically study LLM knowledge materialization using miniGPTKBs (domain-specific, tractable subcrawls), analyzing termination, reproducibility, and robustness across three categories of metrics: yield, lexical similarity, and semantic similarity. We experiment with four variations (seed, language, randomness, model) and three illustrative domains (from history, entertainment, and finance). Our findings show (i) high termination rates, though model-dependent; (ii) mixed reproducibility; and (iii) robustness that varies by perturbation type: high for seeds and temperature, lower for languages and models. These results suggest that LLM knowledge materialization can reliably surface core knowledge, while also revealing important limitations.

Problem

• Studying termination of LLM knowledge extraction processes
• Analyzing reproducibility of structured knowledge outputs
• Evaluating robustness to linguistic and model variations

Innovation

• Systematic analysis of LLM knowledge extraction termination
• Evaluated reproducibility across multiple experimental variations
• Assessed robustness using yield and similarity metrics