arXiv AI June 10, 2026 By Nina I. Shamsi

Density Ridge Selective Prediction for LLM and VLM Hallucination Detection under Calibration Label Scarcity

arXiv:2606. 10198v1 Announce Type: cross Abstract: Hallucination detection in large language and vision-language models is increasingly framed as selective prediction, where a detector assigns a confidence score and abstains when confidence is low.

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 AI.

arXiv AI

Jul 28

TRE: Training-Free Hallucination Detection for Diffusion Language Models

arXiv:2607. 22661v1 Announce Type: new Abstract: Diffusion large language models (D-LLMs) have recently gained increasing attention, yet their reliability is significantly hindered by the hallucination problem.

By Pengcheng Weng, Yanyu Qian, Yue Tan, Yixin Liu

llms diffusion safety

arXiv AI

Jun 9

BEACON: Behavioral Entropy Aggregation for Cross-Model Hallucination Detection in Large Language Models

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.

By Naveen Bera, Pulijala Sai Nikhila, Kondaguduru Abhiram, Shaik Gayaz Ali, Shoaib Sadiq Salehmohamed, Shaik Mohammed Omar, Jinal Prashant Thakkar, Hansika Aredla, Shalmali Ayachit

llms rag benchmarks safety

arXiv AI

Sep 1

Small Updates, Big Doubts: Does Parameter-Efficient Fine-tuning Enhance Hallucination Detection ?

arXiv:2602.11166v2 Announce Type: replace-cross Abstract: Parameter-efficient fine-tuning (PEFT) methods are widely used to adapt large language models (LLMs) to downstream tasks and are often assume...

By Xu Hu, Yifan Zhang, Songtao Wei, Chen Zhao, Qiannan Li, Bingzhe Li, Feng Chen

llms fine-tuning benchmarks safety

arXiv AI

Aug 26

When Do Supervised UQ Ensembles Improve LLM Hallucination Detection? A Robustness Study

arXiv:2608.24492v1 Announce Type: cross Abstract: Uncertainty quantification (UQ) methods are widely used for hallucination detection in large language models (LLMs) in closed-book settings where gro...

By Mohit Singh Chauhan, Vipin Gyanchandani, Dylan Bouchard

llms safety

arXiv AI

Jul 28

D-Score: A Spectral Hidden-State Signal for Hallucination Detection in Large Language Models

arXiv:2607. 24586v1 Announce Type: cross Abstract: Large Language Models can produce fluent text that is false, unsupported by the available evidence, or inconsistent with information that appears to be internally represented by the model.

By Bianca Raimondi, Davide Evangelista, Maurizio Gabbrielli, Elena Loli Piccolomini

llms safety

arXiv AI

Aug 18

HalluTracer: Hallucination Detection via Depth-Averaging Truth Signals

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.

By Zhihao Guo, Zonghan Wu, Huan Huo, DaYong Ye, Junwei Zhang, Weiran Yao, Zhiwei Liu, Qingsong Wen, Yilei Shao

llms efficiency benchmarks safety

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Related stories

TRE: Training-Free Hallucination Detection for Diffusion Language Models

BEACON: Behavioral Entropy Aggregation for Cross-Model Hallucination Detection in Large Language Models

Small Updates, Big Doubts: Does Parameter-Efficient Fine-tuning Enhance Hallucination Detection ?

When Do Supervised UQ Ensembles Improve LLM Hallucination Detection? A Robustness Study

D-Score: A Spectral Hidden-State Signal for Hallucination Detection in Large Language Models

HalluTracer: Hallucination Detection via Depth-Averaging Truth Signals