Abstract:Large language models hallucinate factual claims and struggle to ground their outputs in retrievable evidence, particularly in non-English languages. Existing resources impose a trade-off: structured knowledge bases lack textual grounding, whereas grounded datasets remain small and monolingual. We introduce FactNet, a billion-scale open resource that couples 1.7B Wikidata assertions with 3.01B evidence pointers drawn from 316 native Wikipedia editions. FactNet employs a deterministic construction pipeline, ensuring that every evidence unit is traceable to its source with byte-level precision. We further establish FactNet-Bench, an evaluation suite for Knowledge Graph Completion, Question Answering, and Fact Checking, equipped with systematic leakage controls. Experiments demonstrate that FactNet-Bench differentiates among structural, text-aware, and LLM-integrated methods, and that cross-lingual structure enables knowledge transfer across language tiers.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2602.03417 [cs.CL] |
| (or arXiv:2602.03417v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2602.03417 arXiv-issued DOI via DataCite |
Submission history
From: Yingli Shen [view email]
[v1]
Tue, 3 Feb 2026 11:44:11 UTC (1,307 KB)
[v2]
Thu, 14 May 2026 07:06:58 UTC (1,317 KB)