Beyond Semantic Equivalence: Logical Graphs for LLM Uncertainty Quantification
Large Language Models often produce confidently stated yet unreliable outputs, posing critical challenges for deployment in safety-sensitive applications. Existing uncertainty metrics such as semantic entropy capture agreement at the level of semantic equivalence, but largely ignore the logical relationships between distinct answers. As a result, they tend to overestimate uncertainty and falsely flag hallucinations in settings where generated responses are diverse in form yet logically compatible (e.g., differing only in granularity or specificity). We propose Logical Graph Uncertainty (LGU), a framework that explicitly models implication and incompatibility among answers. LGU aggregates ...
| Authors | Yanni Dong, Minghua Liu, Meilin Zhu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 18 July 2026 |
| Last updated at source | 5 August 2026 |
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