Towards Efficient Pre-Trained Language Model via Feature Correlation Distillation

Published: 21 Sept 2023, Last Modified: 02 Nov 2023NeurIPS 2023 posterEveryoneRevisionsBibTeX
Keywords: Knowledge Distillation; Pre-Trained Language Model
TL;DR: A simple yet effective feature correlation distillation for large Transformer-based PLMs.
Abstract: Knowledge Distillation (KD) has emerged as a promising approach for compressing large Pre-trained Language Models (PLMs). The performance of KD relies on how to effectively formulate and transfer the knowledge from the teacher model to the student model. Prior arts mainly focus on directly aligning output features from the transformer block, which may impose overly strict constraints on the student model's learning process and complicate the training process by introducing extra parameters and computational cost. Moreover, our analysis indicates that the different relations within self-attention, as adopted in other works, involves more computation complexities and can easily be constrained by the number of heads, potentially leading to suboptimal solutions. To address these issues, we propose a novel approach that builds relationships directly from output features. Specifically, we introduce token-level and sequence-level relations concurrently to fully exploit the knowledge from the teacher model. Furthermore, we propose a correlation-based distillation loss to alleviate the exact match properties inherent in traditional KL divergence or MSE loss functions. Our method, dubbed FCD, presents a simple yet effective method to compress various architectures (BERT, RoBERTa, and GPT) and model sizes (base-size and large-size). Extensive experimental results demonstrate that our distilled, smaller language models significantly surpass existing KD methods across various NLP tasks.
Supplementary Material: zip
Submission Number: 3038