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arXiv Computation and Language By Yanyu Qian, Yue Tan, Yixin Liu, Wang Yu, Shirui Pan

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

Read the original on arXiv Computation and Language →

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.

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 Computation and Language.

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