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D-Score: A Spectral Hidden-State Signal for Hallucination Detection in Large Language Models
arXiv cs.AI · Published · 3 min read
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- What happened
- Researchers introduced D-Score, a spectral method to detect hallucinations in large language models by analyzing hidden activation patterns from a single forward pass.
- Why it matters
- Matters for engineers building LLM applications who need hallucination detection without external verifiers, retrieval systems, or multiple model generations.
- Watch out
- Paper is under review and not yet peer-reviewed. Evaluation limited to two datasets. Requires per-model and per-layer calibration with tolerance parameters.
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Computer Science > Computation and Language
arXiv:2607.24586v1 (cs)
[Submitted on 27 Jul 2026]
Title: D-Score: A Spectral Hidden-State Signal for Hallucination Detection in Large Language Models
Authors: Bianca Raimondi , Davide Evangelista , Maurizio Gabbrielli , Elena Loli Piccolomini
View a PDF of the paper titled D-Score: A Spectral Hidden-State Signal for Hallucination Detection in Large Language Models, by Bianca Raimondi and 3 other authors
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Abstract: Large Language Models can produce fluent text that is false, unsupported by the available evidence, or inconsistent with information that appears to be internally represented by the model. We study hallucination detection from the geometry of hidden activations and introduce the D-Score, a simple spectral statistic computed from a single forward pass. For a fixed model, layer, and tolerance parameter, the D-Score counts how many singular directions of the hidden activation matrix have singular values that remain close to the leading one. We use this quantity as a hallucination score, classifying an input text as hallucinated when its D-Score is larger than a pre-defined quantity. The motivation is that, when a model processes a text that conflicts with information available in its own internal state, the hidden representation may encode both the asserted content and some form of count
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- language model
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