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arXiv Computation and Language By Kai Qian, Dongjie Xu, Weijie Shi, Jiaqi Wang, Haolun Ma, Jia Zhu, Jiajie Xu

Decoupling Consistency and Conflict across MLLM Layers for Fake News Detection

Read the original on arXiv Computation and Language →

The paper introduces Multi-Level Consistency and Conflict Decoupling (MLCCD), a method for detecting fake news in multimodal content by leveraging the layered knowledge of multimodal large language models (MLLMs). MLCCD first selects relevant visual and textual tokens using the MLLM’s final decision representation, then identifies tokens whose representations change sharply across adjacent layers to capture layer‑specific knowledge. Finally, it decouples this knowledge into consistency and conflict evidence, aggregating each across layers with dedicated routers that adapt to varying conflict levels, achieving superior performance on three benchmarks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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