Papers by Haixing Wu

1 papers
UMPIRE: Unveiling LLM-generated Posts via Redundant Expressions (2026.acl-long)

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Challenge: Existing methods to verify the provenance of multimodal content fall into two categories: traditional methods rely on low-level artifacts or unimodal statistics.
Approach: They propose a semantic decomposition mechanism that disentangles textual embeddings into redundant and complementary components and a latent redundancy regularization loss to encourage LLM-generated content to exhibit high semantic redundancies.
Outcome: The proposed method outperforms state-of-the-art detection methods across multiple datasets and achieves 5.38% improvement in accuracy.

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