DynHD is a method for detecting hallucinations in diffusion large language models (D‑LLMs) by focusing on token‑level uncertainty and its evolution during the denoising process. It introduces a semantic‑aware evidence construction module that filters out non‑informative structural tokens and highlights uncertainty in informative tokens, and a reference evidence generator that models the expected trajectory of uncertainty, enabling a deviation‑based detector to identify hallucinations. Experiments show DynHD outperforms existing baselines while being more efficient across various benchmarks and backbone models.
By Yanyu Qian, Yue Tan, Yixin Liu, Wang Yu, Shirui Pan
arXiv:2608. 19206v1 Announce Type: cross Abstract: Contemporary Large Language Models (LLMs) are increasingly aligned to suppress hallucinations, prioritizing factual retrieval over combinatorial creativity.
By Nicolas Rodriguez-Alvarez (IES Parquesol, Valladolid, Spain)
arXiv:2609.35860v1 Announce Type: cross
Abstract: Sampling based consistency is widely used for hallucination detection, yet aggregate performance can conceal systematic differences in which errors a...
By Pranav Darshan, Pranav A, Sravan Karthick T, Minal Moharir, Ivan P. Yamshchikov
arXiv:2608. 07525v1 Announce Type: cross Abstract: Hallucination remains a persistent challenge for Multimodal Large Language Models (MLLMs), severely limiting their reliability in high-stakes applications.
By Pengfei Zhou, Jiajun Song, Zhiwei Tang, Yixing Ma, Xiaopeng Peng, Donghui Si, Yuhang Xu, Huiqi Song, Yiyuan Miao, Yichen Qian, Weihua Chen, Wangbo Zhao, Bohan Zhuang, Jiasheng Tang, Yang You
The paper proposes using the temporal volatility of internal attention mechanisms—measured by an unsupervised attention dispersion metric—as a diagnostic signal for hallucinations in large language models. It demonstrates that spikes in attention entropy within intermediate layers correlate with reasoning breakdowns, and shows statistically significant AUC improvements of up to +0.076 over output-based baselines on GSM8K and MATH-500 benchmarks using the Qwen2.5 model family.
By Shardul P. More, Tanuja S. Pawar
arXiv:2608. 06417v1 Announce Type: new Abstract: The proliferation of misinformation online has driven demand for scalable detection systems.
By Pedro Barcelos, Ot\'avio Parraga, Marcelo M. Mussi, Lucas M. Fraga, Lucas S. Kupssinsk\"u, Rodrigo C. Barros
The paper investigates why large language models (LLMs) produce hallucinations—outputs that are fabricated, unverifiable, or contradictory to source material—and argues that these hallucinations have philosophical implications for machine consciousness. It reviews known causes such as source‑target divergence, training‑inference discrepancies, and overfitting, and presents two empirical studies: one showing that higher temperature settings in GPT models yield plausible but incorrect answers, while lower temperatures produce accurate ones; and another demonstrating that an encoder‑only model trained on encyclopedic data answers factually without embellishment, suggesting hallucinations arise from exposure to subjective, socially diverse data rather than cognitive ability. Drawing on Turing, Searle’s Chinese Room, the frame problem, and cybernetic theory, the authors contend that a model’s self‑reports of emotion or sentience fall within the definition of hallucination, implying that any future machine consciousness may remain epistemically inaccessible because it would be indistinguishable from an advanced hallucination.
By Kristina \v{S}ekrst
arXiv:2605. 05686v3 Announce Type: replace Abstract: Language models draw on two knowledge sources: facts baked into weights (parametric memory, PM) and information in context (working memory, WM).
By Qiyao Liang, Risto Miikkulainen, Ila Fiete
The paper introduces a method for identifying the dominant knowledge source behind large language model (LLM) outputs, distinguishing between faithfulness violations (misuse of provided context) and factuality violations (errors in internal knowledge). A simple linear probe trained on hidden representations can reliably classify this source, and the authors present AttriWiki, a self‑supervised pipeline that generates labeled training data by prompting models to recall withheld entities or read them from context. Probes trained on AttriWiki achieve high Macro‑F1 scores across several models and datasets, generalize zero‑shot to a benchmark, and show that attribution mismatches can increase error rates by up to 70%.
"whyItMatters":"The study demonstrates that knowing the source of an LLM’s answer is crucial for effective mitigation of hallucinations, as attribution mismatches significantly raise error rates."
By Ivo Brink, Alexander Boer, Dennis Ulmer
arXiv:2609.32964v2 Announce Type: replace
Abstract: Language models (LMs) often hallucinate by committing to confident answers rather than abstaining, even when they do not have enough information to...
By Vy Nguyen, Ziqi Xu, Jeffrey Chan, Estrid He, Feng Xia, Renqiang Luo, Erik Cambria, Xiuzhen Zhang
arXiv:2604. 10697v2 Announce Type: replace-cross Abstract: Large language models frequently exhibit hallucinations: fluent and confident outputs that are factually incorrect or unsupported by the input context.
By Jakub Binkowski, Kamil Adamczewski, Tomasz Kajdanowicz
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.
By Kingshuk Gupta, Davide Buscaldi