{"slug": "llm-hallucination-detection-via-low-rank-multilinear-decomposition", "title": "LLM Hallucination Detection via Low-rank Multilinear Decomposition", "summary": "A new arXiv paper (2610.36191v1) proposes LOTUS (Low-rank Order-statistic TUcker-based Subspace learning), a multilinear low-rank decomposition framework for detecting hallucinations in large language models by extracting compact representations from hidden states. LOTUS summarizes variable-length token representations with statistical descriptors capturing peak activations, activation distributions and cross-layer changes, then applies low-rank multilinear decomposition to train a lightweight hallucination classifier while reducing dependence on labeled data. The authors report improved hallucination detection over recent state-of-the-art baselines across multiple LLMs and question-answering benchmarks.", "body_md": "arXiv:2610.36191v1 Announce Type: new \nAbstract: Large language models (LLMs) frequently generate fluent yet factually incorrect responses, raising concerns about their reliability. Recent hallucination detectors increasingly exploit internal representations, but extracting informative signals from high-dimensional hidden states remains challenging. Existing approaches often rely on selected activations or pooling-based compression, potentially overlooking discriminative information distributed across Transformer layers, feature modes, and tokens. We propose LOTUS (Low-rank Order-statistic TUcker-based Subspace learning), a multilinear low-rank decomposition framework for hallucination detection that extracts compact representations from LLM hidden states while reducing dependence on labeled data. Our approach first summarizes variable-length token representations using complementary statistical descriptors that capture peak activations, activation distributions, and cross-layer changes. We then leverage low-rank multilinear decomposition to extract compact representations that capture the underlying structure of LLM hidden states, which are subsequently used to train a lightweight hallucination classifier. Experiments across multiple LLMs and question-answering benchmarks demonstrate improved hallucination detection performance over recent state-of-the-art baselines.", "url": "https://wpnews.pro/news/llm-hallucination-detection-via-low-rank-multilinear-decomposition", "canonical_source": "https://arxiv.org/abs/2610.36191", "published_at": "2026-10-28 04:00:00+00:00", "updated_at": "2026-10-28 04:12:41.973486+00:00", "lang": "en", "topics": ["large-language-models", "ai-safety", "ai-research", "machine-learning", "artificial-intelligence"], "entities": ["LOTUS", "arXiv"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/llm-hallucination-detection-via-low-rank-multilinear-decomposition", "markdown": "https://wpnews.pro/news/llm-hallucination-detection-via-low-rank-multilinear-decomposition.md", "text": "https://wpnews.pro/news/llm-hallucination-detection-via-low-rank-multilinear-decomposition.txt", "jsonld": "https://wpnews.pro/news/llm-hallucination-detection-via-low-rank-multilinear-decomposition.jsonld"}}