Weakly Supervised Distillation of Hallucination Signals into Transformer Representations
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arXiv:2604.06277v1 Announce Type: new
Abstract: Existing hallucination detection methods for large language models (LLMs) rely on external verification at inference time, requiring gold answers, retrieval systems, or auxiliary judge models. We ask whether this external supervision can instead be distilled into the model's own representations during training, enabling hallucination detection from internal activations alone at inference time.
We introduce a weak supervision framework that combi
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arXiv:2604.06277v1 Announce Type: new
Abstract: Existing hallucination detection methods for large language models (LLMs) rely on external verification at inference time, requiring gold answers, retrieval systems, or auxiliary judge models. We ask whether this external supervision can instead be distilled into the model's own representations during training, enabling hallucination detection from internal activations alone at inference time.
We introduce a weak supervision framework that combi
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