Where Detectors Fail: Closing the Tail-Domain Gap with Expert-Guided Mutual Distillation

Abstract: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-specific students use mutual learning and dual-channel distillation to inherit the teacher's feature geometry and calibrated predictions while discouraging local domain priors. We further construct Weibo_Balanced, a domain-balanced benchmark that isolates the effect of imbalance on generalization. Across four datasets in two languages, EGMD achieves state-of-the-art accuracy while reducing domain bias by up to 57.3%.

Computation and Language (cs.CL)

arXiv:2607.26555 [cs.CL]

(or arXiv:2607.26555v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.26555 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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