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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.05740 |
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| _version_ | 1866912472030511104 |
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| author | Hu, Yujia Nguyen, Tuan-Phong Ghosh, Shrestha Müller, Moritz Razniewski, Simon |
| author_facet | Hu, Yujia Nguyen, Tuan-Phong Ghosh, Shrestha Müller, Moritz Razniewski, Simon |
| contents | Language models are powerful tools, 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 (Hu et al., ACL 2025). The demonstration experience focuses on three use cases: (1) link-traversal-based LLM knowledge exploration, (2) SPARQL-based structured LLM knowledge querying, (3) comparative exploration of the strengths and weaknesses of LLM knowledge. Massive-recursive LLM knowledge materialization is a groundbreaking opportunity both for the research area of systematic analysis of LLM knowledge, as well as for automated KB construction. The GPTKB demonstrator is accessible at https://gptkb.org. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_05740 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GPTKB v1.5: A Massive Knowledge Base for Exploring Factual LLM Knowledge Hu, Yujia Nguyen, Tuan-Phong Ghosh, Shrestha Müller, Moritz Razniewski, Simon Computation and Language Language models are powerful tools, 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 (Hu et al., ACL 2025). The demonstration experience focuses on three use cases: (1) link-traversal-based LLM knowledge exploration, (2) SPARQL-based structured LLM knowledge querying, (3) comparative exploration of the strengths and weaknesses of LLM knowledge. Massive-recursive LLM knowledge materialization is a groundbreaking opportunity both for the research area of systematic analysis of LLM knowledge, as well as for automated KB construction. The GPTKB demonstrator is accessible at https://gptkb.org. |
| title | GPTKB v1.5: A Massive Knowledge Base for Exploring Factual LLM Knowledge |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2507.05740 |