MedHal-Loc benchmark shows KG-triple hallucination detectors localize errors no better than chance on controlled medical statements due to entity extraction limits, while NLI and consistency methods succeed above chance, and real hallucinations are mostly diffuse conclusion changes.
Grapheval: A knowledge-graph based llm hallucination evaluation framework
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A structured survey organizing graph-LLM integration methods by purpose, modality, and strategy across application domains.
MAGNET multi-agent generation with persona grounding and ATLAS graph verification yields 34-50% fewer hallucinations and annotations than single-model or IBSEN baselines at 100-page scale.
CPIL is a contrastive two-stage method that enforces paraphrase invariance on limited labeled data to outperform baselines in hallucination detection across 11 tasks.
Graphs can help LLMs reduce hallucinations, boost reasoning via prompting techniques, and better process structured data.
citing papers explorer
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MedHal-Loc: Are "Explainable-by-Architecture" Medical Hallucination Detectors Faithful Localizers? A Localization Benchmark
MedHal-Loc benchmark shows KG-triple hallucination detectors localize errors no better than chance on controlled medical statements due to entity extraction limits, while NLI and consistency methods succeed above chance, and real hallucinations are mostly diffuse conclusion changes.
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Integrating Graphs, Large Language Models, and Agents: Reasoning and Retrieval
A structured survey organizing graph-LLM integration methods by purpose, modality, and strategy across application domains.
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From Personas to Plot: Character-Grounded Multi-Agent Story Generation for Long-Form Narratives
MAGNET multi-agent generation with persona grounding and ATLAS graph verification yields 34-50% fewer hallucinations and annotations than single-model or IBSEN baselines at 100-page scale.
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Cross Paraphrastic Invariance Learning for Hallucination Detection
CPIL is a contrastive two-stage method that enforces paraphrase invariance on limited labeled data to outperform baselines in hallucination detection across 11 tasks.
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Position: How can Graphs Help Large Language Models?
Graphs can help LLMs reduce hallucinations, boost reasoning via prompting techniques, and better process structured data.