Auditing a KB Elicitation of Frontier LLM Knowledge: A Multi-dimensional Analysis of GPTKB v1.5
Mirrored from arXiv — NLP / Computation & Language for archival readability. Support the source by reading on the original site.
Computer Science > Computation and Language
Title:Auditing a KB Elicitation of Frontier LLM Knowledge: A Multi-dimensional Analysis of GPTKB v1.5
Abstract:LLMs are remarkable artifacts that have revolutionized a range of knowledge-intensive tasks. A significant contributor is their factual knowledge, which, to date, remains poorly understood, and is usually analyzed from biased samples. In this paper, we provide a framework and the results of a multi-dimensional analysis of GPTKB v1.5 (Hu et al., 2025a), a recursively elicited Knowledge Base (KB) of 100 million facts (or beliefs) of a frontier LLM, namely, GPT-4.1. Given the scale of the elicited facts, we provide a multi-dimensional approach to qualitatively and quantitatively analyze these facts as opposed to the mainstream fact completion benchmarks, which are prone to availability bias. We find that the models' factual knowledge differs quite significantly from established knowledge bases, and that its accuracy is significantly lower than indicated by previous benchmarks. We also find that inconsistency, ambiguity and hallucinations are major issues, shedding light on future research opportunities in neuro-symbolic AI concerning extraction, consolidation and verification of factual LLM knowledge.
| Comments: | Accepted at AKBC@EMNLP 2026 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2510.07024 [cs.CL] |
| (or arXiv:2510.07024v3 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2510.07024
arXiv-issued DOI via DataCite
|
Submission history
From: Luca Giordano [view email][v1] Wed, 8 Oct 2025 13:48:38 UTC (114 KB)
[v2] Thu, 9 Oct 2025 07:23:03 UTC (114 KB)
[v3] Fri, 18 Sep 2026 13:29:31 UTC (105 KB)
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
References & Citations
Bookmark
Bibliographic and Citation Tools
Code, Data and Media Associated with this Article
Demos
Recommenders and Search Tools
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
More from arXiv — NLP / Computation & Language
-
A Mechanistic Study of AI-Text Detection Neurons in Frozen BERT: Sparse Probing and Activation Patching on RAID
Sep 28
-
Manifold Projection and Iterative Autoencoder Refinement for Masked Language Modeling
Sep 28
-
Not All Memories Are Equal: Hierarchical Collaborative Memory for Validity-Aware Retrieval in LLM Agents
Sep 28
-
Auditing and Repairing LLM-as-Judge Failures in a Production Text-to-SQL Pipeline
Sep 28
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.