Multi-Teacher Knowledge Distillation via Teacher-Informed Mixture Priors
Knowledge distillation is a powerful method for model compression, enabling the efficient deployment of complex deep learning models (teachers), including large language models. However, its underlying statistical mechanisms remain unclear, and uncertainty evaluation is often overlooked, especially in real-world scenarios requiring diverse teacher expertise. To address these challenges, we introduce \textit{Multi-Teacher Bayesian Knowledge Distillation} (MT-BKD), where a distilled student model learns from multiple teachers within the Bayesian framework. Our approach leverages Bayesian inference to capture inherent uncertainty in the distillation process. We introduce a teacher-informed p...
| Authors | Luyang Fang, Yongkai Chen, Jiazhang Cai et al. |
|---|---|
| Primary category | stat.ME |
| Published | 27 May 2026 |
| Last updated at source | 27 May 2026 |
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