# Foundations of LLM Knowledge Materialization: Termination, Reproducibility, Robustness

arXiv: https://arxiv.org/abs/2510.06780 · HTML: https://mlsanity.com/paper/2510.06780/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 27 references that are in the map.
- First posted on arXiv 8 Oct 2025.

## Builds on (most cited first, 14 of 14)

- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [gpt-oss-120b & gpt-oss-20b Model Card](https://mlsanity.com/paper/2508.10925/) (2025)
- [How Can We Know What Language Models Know?](https://mlsanity.com/paper/1911.12543/) (2019)
- [Multilingual LAMA: Investigating Knowledge in Multilingual Pretrained Language Models](https://mlsanity.com/paper/2102.00894/) (2021)
- [Crawling the Internal Knowledge-Base of Language Models](https://mlsanity.com/paper/2301.12810/) (2023)
- [Teuken-7B-Base & Teuken-7B-Instruct: Towards European LLMs](https://mlsanity.com/paper/2410.03730/) (2024)
- [Language-Specific Latent Process Hinders Cross-Lingual Performance](https://mlsanity.com/paper/2505.13141/) (2025)
- [Interpreting Language Models Through Knowledge Graph Extraction](https://mlsanity.com/paper/2111.08546/) (2021)
- [Enabling LLM Knowledge Analysis via Extensive Materialization](https://mlsanity.com/paper/2411.04920/) (2024)
- [GPTKB v1.5: A Massive Knowledge Base for Exploring Factual LLM Knowledge](https://mlsanity.com/paper/2507.05740/) (2025)
- [Transforming Scholarly Landscapes: Influence of Large Language Models on Academic Fields beyond Computer Science](https://mlsanity.com/paper/2409.19508/) (2024)

## Built on by (most cited first, 3 of 3)

- [LLMpedia: A Transparent Framework to Materialize an LLM's Encyclopedic Knowledge at Scale](https://mlsanity.com/paper/2603.24080/) (2026)
- [Beyond Questions: Evaluating the Stated Knowledge of LLMs](https://mlsanity.com/paper/2605.26937/) (2026)
- [Perspective: Towards sustainable exploration of chemical spaces with machine learning](https://mlsanity.com/paper/2604.00069/) (2026)

## Abstract (arXiv)

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.