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

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• DynHD proposes a semantic-aware evidence construction module to address information density imbalance across tokens in D-LLMs.method 70%
• DynHD models denoising dynamics for hallucination detection by learning expected uncertainty evolution trajectories.method 70%
• DynHD consistently outperforms state-of-the-art baselines in hallucination detection across multiple benchmarks.result 70%
• DynHD achieves higher efficiency compared to state-of-the-art baselines.result 70%
• Token-level uncertainty, such as entropy, is a widely used signal for detecting factual errors in D-LLMs.technical 70%
• The denoising dynamics approach for hallucination detection could become less effective with evolving D-LLM architectures.limitation 70%
• DynHD requires access to model internals (denoising dynamics), which may be limited with proprietary models.limitation 70%
• The market lacks specialized, efficient detection tools for diffusion large language models (D-LLMs).market 70%

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