Revisiting Knowledge Distillation for Autoregressive Language Models
Qihuang Zhong Liang Ding Li Shen Juhua Liu Bo Du Dacheng Tao
Knowledge distillation (KD) is a common approach to compress a teacher model
to reduce its inference cost and memory footprint, by training a smaller
student model. However, in the context of autoregressive language models (LMs),
we empirically find that larger teacher LMs might dramatically result in a
poorer student. In response to this problem, we conduct a series of analyses
and reveal that different tokens have different teaching modes, neglecting
which will lead to performance degradation. Motivated by this, we propose a
simple yet effective adaptive teaching approach (ATKD) to improve the KD. The
core of ATKD is to reduce rote learning and make teaching more diverse and
flexible. Extensive experiments on 8 LM tasks show that, with the help of ATKD,
various baseline KD methods can achieve consistent and significant performance
gains (up to +3.04% average score) across all model types and sizes. More
encouragingly, ATKD can improve the student model generalization effectively.