arXiv:2507.05740v2 Announce Type: replace Abstract: Language models are powerful artifacts, yet their factual knowledge is still poorly understood, and inaccessible to ad-hoc browsing and scalable statistical analysis. This demonstration introduces GPTKB v1.5, a densely interlinked 100-million-triple knowledge base (KB) built for $14,000 from GPT-4.1, using the GPTKB methodology for massive-recursive LLM knowledge materialization. This demo focuses on three use cases: (1) link-traversal-based LLM knowledge exploration, (2) SPARQL-based structured LLM knowledge querying, (3) comparative explora
The increasing sophistication and scale of LLMs necessitate new tools for understanding and leveraging their internal knowledge, which is critical for future AI development and application.
This development offers a breakthrough in making the factual knowledge encoded within large language models more accessible and analyzable, accelerating research and practical applications.
The ability to systematically query and explore LLM knowledge through structured databases like GPTKB v1.5 transforms how researchers and developers can interact with and understand AI models.
- · AI researchers
- · LLM developers
- · Data scientists
- · Knowledge graph companies
- · Purely black-box AI approaches
Systematic evaluation and improvement of LLM factual accuracy becomes feasible.
New applications emerge that leverage the explicit and queryable knowledge within LLMs.
The development of highly specialized and context-aware AI agents is accelerated through better access to underlying knowledge.
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