Hallucinations in large language models (LLMs) remain a central obstacle to trustworthy deployment, motivating detectors that are accurate, lightweight, and broadly applicable. Since an LLM with a prompt defines a conditional distribution, we argue that the complexity of the distribution is an indicator of hallucination. However, the density of the distribution is unknown and the samples (i.e., responses generated for the prompt) are discrete distributions, which leads to a significant challenge in quantifying the complexity of the distribution. We propose to compute the optimal-transport distances between the sets of token embeddings of pairwise samples, which yields a Wasserstein distance matrix measuring the costs of transforming between the samples. This Wasserstein distance matrix provides a means to quantify the complexity of the distribution defined by the LLM with the prompt. Based on the Wasserstein distance matrix, we derive two complementary signals: AvgWD, measuring the average cost, and EigenWD, measuring the cost complexity. This leads to a training-free detector for hallucinations in LLMs. We further extend the framework to black-box LLMs via teacher forcing with an accessible teacher model. Experiments show that AvgWD and EigenWD are competitive with strong uncertainty baselines and provide complementary behavior across models and datasets, highlighting distribution complexity as an effective signal for LLM truthfulness.
Do LLM hallucination detectors suffer from low-resource effect?
Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）；Ruhr University Bochum（波鸿鲁尔大学）；UAR Research Center for Trustworthy Data Science and Security（UAR可信数据科学与安全研究中心）
Optimal Transport for Unsupervised Hallucination Detection in Neural Machine Translation
Instituto de Telecomunicações（电信研究所）；Instituto Superior Técnico（高等技术学院）；University of Lisbon（里斯本大学）；CentraleSupélec（中央理工-高等电力学院）；Université Paris-Saclay（巴黎萨克雷大学）；CNRS（法国国家科学研究中心）；Unbabel（Unbabel公司）
Hallucination Detection-Guided Preference Optimization for Clinical Summarization
Manning College of Information and Computer Sciences（Manning信息与计算机科学学院）；Ensemble HP；Columbia University（哥伦比亚大学）；University of Massachusetts Amherst（马萨诸塞大学阿姆赫斯特分校）
TriLens: Per-Layer Logit-Lens Entropy for White-Box Hallucination Detection
Binjiang Institute of Zhejiang University（浙江大学滨海学院）；Beijing Normal-Hong Kong Baptist University（北京师范大学-香港 Baptist大学）；Zhejiang University（浙江大学）；GenTel.io；Great Bay University（Great Bay大学）
DHCP: Detecting Hallucinations by Cross-modal Attention Pattern in Large Vision-Language Models
Tsinghua University, Tencent（清华大学、腾讯）；Tencent（腾讯）；Tencent, University of Macau（腾讯、澳门大学）；University of Science and Technology Beijing（北京科技大学）；Tsinghua University（清华大学）
The System Hallucination Scale (SHS): A Minimal yet Effective Human-Centered Instrument for Evaluating Hallucination-Related Behavior in Large Language Models
Machine Learning and Information Science Group, Medical University of Graz（格拉茨医科大学机器学习与信息科学组）；Human Machine Mind Cooperation, Graz, Austria（格拉茨人类机智合作中心）；MIDATA Cooperative, Zurich, Switzerland（苏黎世MIDATA合作组织）；Human-Centered AI Lab, BOKU University Vienna（维也纳BOKU大学人本AI实验室）
Can LLMs Detect Intrinsic Hallucinations in Paraphrasing and Machine Translation?
RISE Research Institutes of Sweden, Department of Computer Science（瑞典RISE研究机构，计算机科学系）；Uppsala University, Department of Linguistics and Philology（乌普萨拉大学，语言学与哲学系）；KTH Royal Institute of Technology, Division of Software and Computer Systems（皇家理工学院，软件与计算机系统系）；University of Edinburgh, School of Informatics（爱丁堡大学，信息学院）
A Survey on Large Language Model Hallucination via a Creativity Perspective
Institute of Computing Technology, CAS（中国科学院计算技术研究所）；University of Chinese Academy of Science（中国科学院大学）；International Digital Economy Academy, IDEA Research（国际数字经济学院IDEA研究院）
V-ITI: Mitigating Hallucinations in Multimodal Large Language Models via Visual Inference-Time Intervention
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）；Baidu Inc.（百度公司）
