Lifting the Curse of Capacity Gap in Distilling Language Models
Chen Zhang Yang Yang Jiahao Liu Jingang Wang Yunsen Xian Benyou Wang Dawei Song
Pretrained language models (LMs) have shown compelling performance on various
downstream tasks, but unfortunately they require a tremendous amount of
inference compute. Knowledge distillation finds a path to compress LMs to small
ones with a teacher-student paradigm. However, when the capacity gap between
the teacher and the student is large, a curse of capacity gap appears, invoking
a deficiency in distilling LMs. While a few studies have been carried out to
fill the gap, the curse is not yet well tackled. In this paper, we aim at
lifting the curse of capacity gap via enlarging the capacity of the student
without notably increasing the inference compute. Largely motivated by sparse
activation regime of mixture of experts (MoE), we propose a mixture of minimal
experts (MiniMoE), which imposes extra parameters to the student but introduces
almost no additional inference compute. Experimental results on GLUE and CoNLL
demonstrate the curse of capacity gap is lifted by the magic of MiniMoE to a
large extent. MiniMoE also achieves the state-of-the-art performance at small
FLOPs compared with a range of competitive baselines. With a compression rate
as much as $\sim$50$\times$, MiniMoE preserves $\sim$95\% GLUE score of the
teacher.