The paper introduces CHORD, a relational graph discrimination framework designed to detect hallucinations across different large language models (LLMs). It addresses the challenge of cross-model representation heterogeneity by modeling hidden states as joint relational graphs over layers, tokens, and features, and employs relation-aware attention and meta-learning to extract transferable detection signals. Experiments demonstrate that CHORD outperforms existing methods in cross-model generalization.
arXiv:2608. 10835v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) achieve impressive visual reasoning and dialogue capabilities, yet frequently hallucinate content unsupported by the visual input.
The paper proposes a hidden‑state probing method for detecting hallucinations at the span level in large language model outputs, moving beyond token‑wise binary classification. By examining layer‑wise activation patterns, the approach identifies the exact onset and continuation tokens of hallucinations, achieving higher precision‑recall AUC than random baselines despite class imbalance. Additionally, the authors introduce a cross‑model detection framework where one model observes another’s internal representations, showing that an external observer can match or surpass the generator’s own self‑detection of hallucination onsets, even when the observer is smaller.
arXiv:2608. 16353v1 Announce Type: cross Abstract: Even well-aligned large language models confidently generate factually incorrect text, making hallucination a persistent reliability risk in high-stakes deployments.
arXiv:2606. 07528v1 Announce Type: cross Abstract: Hallucination in large language models (LLMs), defined as the generation of factually incorrect or unsupported content, remains a critical barrier to reliable deployment.
arXiv:2509. 24770v2 Announce Type: replace Abstract: Large Language Models (LLMs) often generate incorrect or unsupported content, known as hallucinations.
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.
The paper introduces InnerExpert, a method that uses Mixture-of-Experts (MoE) internal signals—such as router entropy, expert disagreement, and usage patterns—to detect hallucinations at the token level in large language models. By combining these MoE-specific signals with standard transformer features into compact per-token vectors, InnerExpert trains a lightweight detector using an LLM-as-a-judge pipeline, enabling continuous updates without manual labeling. Experiments across five datasets and two MoE architectures show that InnerExpert outperforms existing methods, achieving up to 0.91 answer-level and 0.76 token-level AUROC with only a single forward pass.
arXiv:2605. 26366v3 Announce Type: replace 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).
The paper introduces InnerExpert, a method that uses Mixture-of-Experts (MoE) architecture signals—such as router entropy, expert disagreement, and usage patterns—to detect hallucinations at the token level in Large Language Models. By combining these MoE-specific signals with standard transformer features into compact per-token vectors, InnerExpert trains a lightweight detector using an LLM-as-a-judge pipeline, enabling continuous updates without manual labeling. Experiments across five datasets and two MoE architectures show that InnerExpert outperforms existing methods, achieving up to 0.91 answer-level and 0.76 token-level AUROC with only a single forward pass.
arXiv:2610.33442v1 Announce Type: new Abstract: Large language models (LLMs) are reported to be susceptible to reliability issues including hallucinations and reasoning errors. Existing quality detec...
arXiv:2608. 08024v1 Announce Type: cross Abstract: Large language models (LLMs) can generate fluent and useful responses but remain prone to hallucinations.
arXiv:2602. 02888v2 Announce Type: replace-cross Abstract: Hallucinations remain a major obstacle for large language models (LLMs), especially in safety-critical domains.