Concept Fields model text corpora as local Gaussian drift fields in embedding space to score sentence transitions for hallucination detection and novelty via standardized deviation.
Tianyun Yang, Ziniu Li, Juan Cao, and Chang Xu
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SHADE adaptively combines coverage and spectral signals to estimate semantic alphabet size from few LLM samples, yielding better performance than baselines in low-sample regimes for alphabet estimation and QA error detection.
Cross-model semantic disagreement adds an epistemic uncertainty term that improves total uncertainty estimation over self-consistency alone, helping flag confident errors in LLMs.
EnsemHalDet improves VLM hallucination detection by ensembling independent detectors trained on diverse internal states, yielding higher AUC than single-detector baselines across VQA datasets.
citing papers explorer
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Text Corpora as Concept Fields: Black-Box Hallucination and Novelty Measurement
Concept Fields model text corpora as local Gaussian drift fields in embedding space to score sentence transitions for hallucination detection and novelty via standardized deviation.
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Mind the Unseen Mass: Unmasking LLM Hallucinations via Soft-Hybrid Alphabet Estimation
SHADE adaptively combines coverage and spectral signals to estimate semantic alphabet size from few LLM samples, yielding better performance than baselines in low-sample regimes for alphabet estimation and QA error detection.
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Complementing Self-Consistency with Cross-Model Disagreement for Uncertainty Quantification
Cross-model semantic disagreement adds an epistemic uncertainty term that improves total uncertainty estimation over self-consistency alone, helping flag confident errors in LLMs.
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EnsemHalDet: Robust VLM Hallucination Detection via Ensemble of Internal State Detectors
EnsemHalDet improves VLM hallucination detection by ensembling independent detectors trained on diverse internal states, yielding higher AUC than single-detector baselines across VQA datasets.