Lifting the Curse of Capacity Gap in Distilling Language Models
This addresses the challenge of efficiently compressing large language models for deployment, though it is incremental as it builds on existing distillation and MoE techniques.
The paper tackles the problem of knowledge distillation for language models when there is a large capacity gap between teacher and student, which leads to performance deficiencies. They propose MiniMoE, a mixture of minimal experts that adds parameters without increasing inference compute, achieving state-of-the-art performance with a 50x compression rate while preserving 95% of the teacher's GLUE score.
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.