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arXiv AI By Igor Itkin

Temporal Multi-Signal Fusion for Token-Level Hallucination Detection

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The paper introduces a token‑level hallucination detector that treats hallucinations as temporally extended spans and uses sequence labeling. It fuses 33‑dimensional features from text statistics, NLI entailment, and language‑model surprisal, and applies a BiGRU to achieve an AUC of 0.840 on RAGTruth, outperforming a logistic‑regression baseline by 11 points. The study shows that temporal ordering of features, rather than model capacity, drives most of the performance gain, and the detector remains effective on unseen language models with less than 4% AUC loss.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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