Papers by Zhengnan Guo

1 papers
Lost in Diffusion: Uncovering Hallucination Patterns and Failure Modes in Diffusion Large Language Models (2026.findings-acl)

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Challenge: dLLMs have emerged as a promising non-autoregressive paradigm for text generation, but their hallucination mechanisms remain underexplored.
Approach: They present the first controlled comparative study to evaluate hallucination patterns in Diffusion Large Language Models.
Outcome: The proposed model exhibits higher propensity for hallucination than AR counterparts controlled for architecture, scale, and pre-training weights.

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