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

TL;DR

DynHD is a novel method for hallucination detection in diffusion large language models that leverages both token-level semantic signals and the evolution of uncertainty during denoising to improve reliability.

Contribution

It introduces a semantic-aware evidence construction and a deviation-based detection approach that models denoising dynamics for more accurate hallucination detection.

Findings

Outperforms state-of-the-art baselines in multiple benchmarks

Achieves higher detection accuracy and efficiency

Effectively models denoising dynamics for hallucination detection

Abstract

Diffusion large language models (D-LLMs) have emerged as a promising alternative to auto-regressive models due to their iterative refinement capabilities. However, hallucinations remain a critical issue that hinders their reliability. To detect hallucination responses from model outputs, token-level uncertainty (e.g., entropy) has been widely used as an effective signal to indicate potential factual errors. Nevertheless, the fixed-length generation paradigm of D-LLMs implies that tokens contribute unevenly to hallucination detection, with only a small subset providing meaningful signals. Moreover, the evolution trend of uncertainty throughout the diffusion process can also provide important signals, highlighting the necessity of modeling its denoising dynamics for hallucination detection. In this paper, we propose DynHD that bridge these gaps from both spatial (token sequence) and temporal (denoising dynamics) perspectives. To address the information density imbalance across tokens, we propose a semantic-aware evidence construction module that extracts hallucination-indicative signals by filtering out non-informative tokens and emphasizing semantically meaningful ones. To model denoising dynamics for hallucination detection, we introduce a reference evidence generator that learns the expected evolution trajectory of uncertainty evidence, along with a deviation-based hallucination detector that makes predictions by measuring the discrepancy between the observed and reference trajectories. Extensive experiments demonstrate that DynHD consistently outperforms state-of-the-art baselines while achieving higher efficiency across multiple benchmarks and backbone models.