arXiv Computation and Language
Aug 25

DynHD: Hallucination Detection for Diffusion Large Language Models via Denoising Dynamics Deviation Learning

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