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arXiv Computation and Language By Devansh Sharma, Faizul Rakib Sayem, Shahana Ibrahim

LLM Hallucination Detection via Low-rank Multilinear Decomposition

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The paper introduces LOTUS, a low‑rank multilinear decomposition method for detecting hallucinations in large language models. LOTUS compresses variable‑length token representations into compact features using statistical descriptors of peak activations, distributions, and cross‑layer changes, then applies low‑rank decomposition to capture the underlying structure of hidden states. Experiments on several LLMs and QA benchmarks show that LOTUS outperforms recent state‑of‑the‑art hallucination detectors while requiring less labeled data.

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 Computation and Language.

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