TriQua: Reconciling Granularity and Context in Factuality Evaluation

2026-08-05

AI Summary

This work addresses the trade-off between granularity and context in factuality evaluation of large language models: atomic facts often lack contextual nuance, while broad statements resist fine-grained assessment. To reconcile this, the authors propose TriQua, a framework that adaptively models factual claims according to their complexity—representing simple assertions as standard triples and encoding complex ones as hyper-relational facts enriched with contextual qualifiers. This approach preserves atomicity while retaining essential context, enabling interpretable, fine-grained error localization. The framework further introduces TriQuaScore, a metric for quantifying factuality at the level of structured factual units. Experiments demonstrate that TriQua achieves robust performance in decomposition quality and alignment with human annotations, outperforming existing methods on evidence-based fact verification tasks.

Abstract

The "decompose-then-verify" paradigm for LLM factuality evaluation faces a fundamental trade-off: atomic facts, i.e., one sentence conveying one unit of information, often omit essential context, while broader statements lack the granularity needed for precise assessment. To address this, we introduce TriQua, a framework that flexibly models facts based on their complexity. Simple claims are extracted as standard triples, while complex claims are represented as hyperrelational facts by attaching auxiliary contextual qualifiers. This adaptive structure preserves the necessary context for accurate retrieval and verification without sacrificing atomicity. Furthermore, TriQua's verification process directly annotates concrete errors within specific triples and qualifiers, providing fine-grained explainability for error detection. Alongside the framework, we propose TriQuaScore to quantify the factuality of these structured fact units. Empirical evaluations show that TriQuaScore strongly aligns with human annotated factuality scores, TriQua achieves robust decomposition quality, and outperforms existing decomposition-based frameworks in evidence-based fact verification.

Problem

• factuality evaluation
• decompose-then-verify
• atomic facts
• context
• granularity

Innovation

• factuality evaluation
• adaptive fact representation
• hyperrelational facts
• fine-grained explainability
• TriQuaScore