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This paper presents HallDetect, a lightweight and reference-free framework for hallucination detection that decomposes generated content into atomic claims and verifies them with a compact entailment model. It outperforms comparably resourced baselines on multiple benchmarks and provides a claim-to-span audit trail.
This paper shows that per-chunk verification fails for multi-hop RAG because no single chunk is sufficient, and proposes decomposition-based verification to repair it, demonstrating significant improvements across multiple datasets.
This paper proposes Logical Graph Uncertainty (LGU), a framework that models implication and incompatibility among answers to improve uncertainty estimation in LLMs, outperforming semantic entropy baselines by up to 7.1% AUROC and 3.5% AUARC across benchmarks.