Automatic Layer Selection for Hallucination Detection
A new study proposes an automated method for selecting layers in large language models to improve hallucination detection. The authors introduce a criterion called First Effective Peak of Intrinsic Dimension (FEPoID), which outperforms existing methods. Additionally, they present a truncation strategy that enhances the detection performance of hallucination-related signals.
- ▪Hallucination-related signals are more strongly encoded in intermediate layers of large language models.
- ▪The study proposes several hypotheses and evaluates criteria for automatic layer selection, finding none consistently satisfactory.
- ▪FEPoID is a training-free selection criterion that identifies optimal layers and incurs negligible computational overhead.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.26366 |
| Publication time | Wed, 27 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-27T04:07:56.398Z |
| Last seen | 2026-05-27T04:07:56.398Z |
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Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.26366 (cs) [Submitted on 25 May 2026] Title:Automatic Layer Selection for Hallucination Detection Authors:Xinpeng Wang, William Cao, Andrew Gordon Wilson, Zhe Zeng View a PDF of the paper titled Automatic Layer Selection for Hallucination Detection, by Xinpeng Wang and 3 other authors View PDF HTML (experimental) Abstract:Recent studies on hallucination detection have shown that hallucination-related signals are more strongly encoded in intermediate layers than in the final layer of large language models (LLMs).
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