ALP-KD: Attention-Based Layer Projection for Knowledge Distillation
This work provides an incremental improvement to knowledge distillation techniques, specifically for researchers and practitioners working on compressing large language models while maintaining performance.
This paper addresses the problem of knowledge distillation for intermediate layers in neural networks, where a student network learns from a teacher network's internal representations. The authors propose an attention-based combinatorial technique to fuse teacher-side information and distill it to student layers, even when there isn't a one-to-one layer alignment. They successfully distilled a 12-layer BERT into smaller versions (6-, 4-, and 2-layer) and demonstrated superior performance on GLUE tasks compared to existing methods.
Knowledge distillation is considered as a training and compression strategy in which two neural networks, namely a teacher and a student, are coupled together during training. The teacher network is supposed to be a trustworthy predictor and the student tries to mimic its predictions. Usually, a student with a lighter architecture is selected so we can achieve compression and yet deliver high-quality results. In such a setting, distillation only happens for final predictions whereas the student could also benefit from teacher's supervision for internal components. Motivated by this, we studied the problem of distillation for intermediate layers. Since there might not be a one-to-one alignment between student and teacher layers, existing techniques skip some teacher layers and only distill from a subset of them. This shortcoming directly impacts quality, so we instead propose a combinatorial technique which relies on attention. Our model fuses teacher-side information and takes each layer's significance into consideration, then performs distillation between combined teacher layers and those of the student. Using our technique, we distilled a 12-layer BERT (Devlin et al. 2019) into 6-, 4-, and 2-layer counterparts and evaluated them on GLUE tasks (Wang et al. 2018). Experimental results show that our combinatorial approach is able to outperform other existing techniques.