MultiHaluDet: Multilingual Hallucination Detection via LLM Hidden State Probing
Hallucinations in Large Language Models (LLMs) represent a critical barrier to their reliable deployment, a vulnerability heavily exacerbated in non-English and resource-constrained contexts. Existing detection approaches that rely on output confidence heuristics or single-layer internal representations frequently fail to capture deep, complex factual inconsistencies across diverse languages. To address this, we introduce MultiHaluDet, a novel three-stage stacking framework that detects multilingual hallucinations by probing the full hidden state trajectories of frozen LLMs without requiring language-specific fine-tuning. Our method extracts sequential features across multiple layers and ...
| Authors | Riasad Alvi, Nurul Labib Sayeedi, Md. Faiyaz Abdullah Sayeedi |
|---|---|
| Primary category | cs.CL |
| Published | 24 May 2026 |
| Last updated at source | 24 May 2026 |
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