Where Detectors Fail: Closing the Tail-Domain Gap with Expert-Guided Mutual Distillation
Multimodal fake news detectors often generalize poorly across domains because they learn to trust unreliable evidence: domain-specific shortcuts amplified by imbalanced data and semantically inconsistent text-image pairs that make cross-modal evidence unreliable. We propose Expert-Guided Mutual Distillation (EGMD), which learns what evidence to trust across the prediction pipeline. At the input level, input-level calibration encodes pair-level coherence as a shared gain before fusion. At the representation level, an expert-guided teacher aligns domain statistics and encourages domain-specific patterns to concentrate in specialized experts. At the decision level, prototype-anchored domain-...
| Authors | Xuan Feng, Guihong Liu, Tianlong Gu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 29 July 2026 |
| Last updated at source | 29 July 2026 |
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