[2603.22303] Sample Transform Cost-Based Training-Free Hallucination Detector for Large Language Models

Computer Science > Machine Learning arXiv:2603.22303 (cs) [Submitted on 17 Mar 2026] Title:Sample Transform Cost-Based Training-Free Hallucination Detector for Large Language Models Authors:Zeyang Ding, Xinglin Hu, Jicong Fan View a PDF of the paper titled Sample Transform Cost-Based Training-Free Hallucination Detector for Large Language Models, by Zeyang Ding and 2 other authors View PDF HTML (experimental) Abstract: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 trai...