Off-the-shelf LLMs match or exceed inter-examiner agreement on a new 32k-response double-marked GCSE dataset spanning five subjects and handwritten scripts.
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MT-BKD applies Bayesian inference with teacher-informed mixture priors and entropy weighting to distill knowledge from multiple teachers, yielding improved accuracy and uncertainty quantification on synthetic and real tasks.
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Multi-Teacher Knowledge Distillation via Teacher-Informed Mixture Priors
MT-BKD applies Bayesian inference with teacher-informed mixture priors and entropy weighting to distill knowledge from multiple teachers, yielding improved accuracy and uncertainty quantification on synthetic and real tasks.