It is found that the models'factual knowledge differs quite significantly from established knowledge bases, and that its accuracy is significantly lower than indicated by previous benchmarks, shedding light on future research opportunities in neuro-symbolic AI concerning extraction, consolidation and verification of fa...
Shrestha Ghosh, Luca Giordano, Yujia Hu et al.· 2 citations
This work proposes GPTKB 2.0, a methodology for constructing disambiguated KBs directly from large language models (LLMs) that incorporates on-the-fly disambiguation of entities, relations and classes, and is meticulously designed to satisfy both scalability and disambiguation accuracy.
Yujia Hu, Tuan-Phong Nguyen, S. Razniewski· 0 citations
GPTKB 2.0 performs context-guided disambiguation during recursive KB construction, separating homonyms and merging synonymous mentions as facts are elicited, and makes this process inspectable: users can browse entities, follow links across the KB, and audit the provenance of individual facts.
Yujia Hu, Tuan-Phong Nguyen, Simon Razniewski· 0 citations
The REAP system combines structured chain-of-thought reasoning, relation-specific query strategies, and a reasoning-based empty-set gate to elicit parametric knowledge, followed by direct extraction into valid JSON arrays for AKBC Shared Task 2026.
Automated Knowledge Base Construction (AKBC) is a core NLP task, and recent work proposes generating knowledge bases directly from large language models (LLMs), treating the model itself as the knowledge source. However, LLMs natively possess no representation of entities, leading to duplicate entries as well as confla...
Yujia Hu, Tuan-Phong Nguyen, S. Razniewski· 0 citations
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