Fan Xu

Diffusion Large Language Models (DLLMs) generate text via iterative masked-token denoising, supporting parallel prediction and bidirectional context modeling. Despite these advantages, decoding remains challenging: many tokens appear predictable early, yet single-step predictions are often unstab...

The increasing reliance on natural language generation (NLG) models, particularly large language models, has raised concerns about the reliability and accuracy of their outputs. A key challenge is hallucination, where models produce plausible but incorrect information. As a result, hallucination...

Current large language models (LLMs) often suffer from hallucination issues, i,e, generating content that appears factual but is actually unreliable. A typical hallucination detection pipeline involves response decomposition (i.e., claim extraction), query generation, evidence collection (i.e., s...