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Drowzee: Metamorphic Testing for Fact-Conflicting Hallucination Detection in Large Language Models
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Outgoing claims: the papers this work cites, builds on, or argues with.
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What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Using Logic Identifying Existence Systems (LIES) to Identify Certain Types of Hallucinations in Large Language Models
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Re-KGR: Mitigating Factual Hallucinations via Hybrid Graph Reasoning and Semantic Refinement
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Do the Instruction-Fine-Tuned Large Language Models Challenge Flawed Instructions? A Study on Over-Compliance and Hallucinations
inferred← citeshttps://doi.org/10.1007/s10586-025-05891-zopenalex-snapshot
The rise of hallucination in large language models: systematic reviews, performance analysis and challenges
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Safety Evaluation for Large Language Models with Metamorphic Testing: An Empirical Study
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Persona-centric Metamorphic Relation Guided Robustness Evaluation for Multi-turn Dialogue Modeling
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A Systematic Literature Review of Hallucinations in Large Language Models
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Hallucination Detection in Large Language Models with Metamorphic Relations
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Beyond Correctness: Exposing LLM-generated Logical Flaws in Reasoning via Multi-step Automated Theorem Proving
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DefAn: Definitive Answer Dataset for LLM Hallucination Evaluation
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