TriQua: Reconciling Granularity and Context in Factuality Evaluation
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 p...
| Authors | Jin Liu, Steffen Thoma, Achim Rettinger |
|---|---|
| Primary category | cs.AI |
| Published | 5 August 2026 |
| Last updated at source | 5 August 2026 |
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