LLMs keep getting more fluent—but can you actually verify what they say? Structured KBs like Wikidata lack text grounding. Annotation-based datasets like FEVER are too small and monolingual. Synthetic expansion just produces hallucinations at scale. The trilemma between authenticity, scale, and structure has gone unsolved. ❓
Today, we dive into FactNet—a landmark contribution by @TsinghuaNLP (OpenBMB member) alongside researchers from TU Munich, Modelbest Inc., and Minzu University of China. FactNet constructs a billion-scale, open-source multilingual knowledge graph that unifies structured Wikidata assertions with auditable, byte-level evidence pointers from 316 native Wikipedia editions.
🤗 Paper: https://huggingface.co/papers/2602.03417
📄 arXiv: https://arxiv.org/abs/2602.03417
💻 Code & Data: https://github.com/yl-shen/factnet

Why it matters:
1⃣️ Billion-Scale & Truly Multilingual: FactNet unifies 1.7B atomic assertions into 1.55B FactSynsets, backed by 3.01B grounded evidence spans across 316 languages. Even the bottom-200 languages hold 2.7% of all evidence—a scale no prior resource has achieved with native, auditable text grounding.
2⃣️ Byte-Level Provenance, Zero Stochastic Inference: Unlike synthetic datasets that sever the connection to authentic sources, FactNet is built through a fully deterministic three-stage pipeline. Every FactSense carries a recoverable pointer (page ID, revision ID, Unicode character offsets), achieving 99.63% exact re-localization on a 1M-sample test.
3⃣️ 92.1% Grounding Precision Across 316 Languages: Human audit of 4,200 items confirms design-weighted precision of 0.921 (95% CI [0.913, 0.929]). WIKILINK_ENTITY and INFOBOX_FIELD matchers cover 55% of evidence at precision above 0.94. Low-resource languages still achieve 0.885—validating deterministic segmentation for tail languages.
4⃣️ FactNet-Bench Sets a New Evaluation Standard: Three tasks (KGC, MKQA, MFC) explicitly penalize leakage—removing predicate masking alone inflates KGC MRR anomalously from 0.298 to 0.351. Grammar-guided decoding boosts valid parse rate from 88.5% to 95.2% on MKQA. MFC Top-5 aggregation reaches 0.73 accuracy and 0.54 Span F1.
FactNet resolves the authenticity-scale-structure trilemma and builds the foundation for AI systems that are not just knowledgeable, but structurally grounded and inherently verifiable.
#AI #THUNLP #OpenBMB #KnowledgeGraph #FactChecking #NLP #LLM #MultilingualAI