Weight-Inherited Distillation for Task-Agnostic BERT Compression Analysis

Keskeiset käsitteet

Weight-Inherited Distillation (WID) offers a novel approach to compressing BERT models without the need for additional alignment losses, showcasing superior performance in task-agnostic settings.

Tiivistelmä

The content discusses the Weight-Inherited Distillation (WID) method for compressing BERT models without requiring extra alignment losses. It introduces the concept of inheriting weights directly from the teacher model to train a compact student model. The paper outlines the process of WID, including structural re-parameterization and compactor compression strategies. Experimental results on GLUE and SQuAD benchmarks demonstrate that WID outperforms traditional KD-based baselines. Further analysis shows that WID can learn high-level semantic knowledge such as attention patterns from the teacher model.

Directory:

1. Abstract
   • Introduces Knowledge Distillation (KD) and proposes Weight-Inherited Distillation (WID).
2. Introduction
   • Discusses Transformer-based Pre-trained Language Models (PLMs) and challenges in storage and computation.
3. Approach
   • Describes how WID directly transfers knowledge by inheriting weights without alignment losses.
4. Experiments
   • Details experiments on downstream NLP tasks with different student model sizes.
5. Results
   • Compares WID with other distillation methods in terms of performance and parameter efficiency.
6. Analysis and Discussion
   • Explores topics like pruning vs. WID, MHA strategies, impact of teacher models, and visualization of attention distributions.
7. Related Work
   • Mentions related work on BERT compression techniques and knowledge distillation.

Tilastot

"Experimental results on the GLUE and SQuAD benchmarks show that WID outperforms previous state-of-the-art KD-based baselines."
"WID retains 98.9% and 90.9% performance of BERTbase using only 49.2% and 10.2% parameters, respectively."

Lainaukset

"In this paper, we propose a novel Weight-Inherited Distillation (WID), which directly transfers knowledge from the teacher."
"Our contributions can be summarized as follows: We propose Weight-Inherited Distillation (WID), revealing a new pathway to KD by directly inheriting the weights via structural re-parameterization."

Tärkeimmät oivallukset

Weight-Inherited Distillation for Task-Agnostic BERT Compression

by Taiqiang Wu,... klo arxiv.org 03-21-2024

https://arxiv.org/pdf/2305.09098.pdf

Syvällisempiä Kysymyksiä

How does Weight-Inherited Distillation compare to traditional pruning methods?

Weight-Inherited Distillation (WID) differs from traditional pruning methods in its approach to compressing neural networks. While pruning typically involves removing redundant weights, WID focuses on directly transferring knowledge from a teacher model to a student model by inheriting the weights through structural re-parameterization. In contrast, traditional pruning methods may result in sub-networks without a predefined structure, while WID maintains the original structure of the network but compresses it by learning mappings from the teacher.

What are the implications of dropping heads versus reducing dimensions in Multi-Head Attention?

When considering Multi-Head Attention (MHA) in models like BERT, there are two strategies for compression: dropping heads and reducing dimensions. Dropping heads involves decreasing the number of attention heads A, while reducing dimensions decreases the size of each head dk. The implications of these strategies depend on factors such as representation subspace capacity and task requirements. For instance, dropping heads can lead to better performance under certain constraints or limitations compared to reducing dimensions which might limit representation capabilities.

How can Weight-Inherited Distillation be applied to larger language models beyond BERT?

Weight-Inherited Distillation (WID) can be extended to larger language models beyond BERT by following similar principles but adapting them to suit the scale and complexity of these models. When applying WID to larger language models, considerations should include optimizing computational resources for training compact student models efficiently while preserving performance levels comparable to their larger counterparts. Additionally, exploring advanced techniques for scaling up WID implementation and addressing challenges specific to large-scale language models will be crucial for successful application across different architectures and tasks.