RAGognizer adds a detection head to LLMs for joint training on generation and token-level hallucination detection, yielding SOTA detection and fewer hallucinations in RAG while preserving output quality.
arXiv preprint arXiv:2502.17125 , year=
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LUCID detects hallucinations in LLM-KG reasoning by extracting node/edge features from attention and semantics then integrating them with KG structure in a GNN, achieving SOTA on nine new benchmark datasets versus 15 baselines.
Human adjudication of conflicts between original benchmark labels and LLM predictions on QAGS-C and SummEval increases triple agreement by 6-8% and LLM accuracy by 2-9%, with adjudicators often siding with models that provide explicit reasoning.
citing papers explorer
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RAGognizer: Hallucination-Aware Fine-Tuning via Detection Head Integration
RAGognizer adds a detection head to LLMs for joint training on generation and token-level hallucination detection, yielding SOTA detection and fewer hallucinations in RAG while preserving output quality.
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Detecting Hallucinations for Large Language Model-based Knowledge Graph Reasoning
LUCID detects hallucinations in LLM-KG reasoning by extracting node/edge features from attention and semantics then integrating them with KG structure in a GNN, achieving SOTA on nine new benchmark datasets versus 15 baselines.
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Do Benchmarks Underestimate LLM Performance? Evaluating Hallucination Detection With LLM-First Human-Adjudicated Assessment
Human adjudication of conflicts between original benchmark labels and LLM predictions on QAGS-C and SummEval increases triple agreement by 6-8% and LLM accuracy by 2-9%, with adjudicators often siding with models that provide explicit reasoning.