aiqbench analysis
This research introduces Logical Graph Uncertainty (LGU), a novel framework for quantifying LLM reliability by modeling logical entailment chains rather than relying solely on semantic equivalence. By penalizing mutual incompatibility among generated answers, the method significantly reduces false hallucination flags in safety-critical applications where diverse but logically consistent responses occur. This advancement addresses a key gap in current uncertainty metrics that often overestimate risk due to superficial differences in answer granularity or specificity.
Beyond Semantic Equivalence: Logical Graphs for LLM Uncertainty Quantification
arXiv:2607.16868v1 Announce Type: new Abstract: Large Language Models (LLMs) 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 probability mass along entailment chains, computes entropy over logically maximal hypotheses, and penalizes mutual incompatibility among them. Across multiple question-answering benchmarks, LGU consistently improves uncertainty estimation over existing methods, and outperforms the semantic entropy baseline by up to +7.1% AUROC and +3.5% AUARC across datasets.
Abstract:Large Language Models (LLMs) 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 probability mass along entailment chains, computes entropy over logically maximal hypotheses, and penalizes mutual incompatibility among them. Across multiple question-answering benchmarks, LGU consistently improves uncertainty estimation over existing methods, and outperforms the semantic entropy baseline by up to +7.1% AUROC and +3.5% AUARC across datasets.
14 pages, 5 figures, 5 tables. Under review
Artificial Intelligence (cs.AI)
arXiv:2607.16868 [cs.AI]
(or arXiv:2607.16868v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.16868 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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