AD-KD: Attribution-Driven Knowledge Distillation for Language Model Compression

• URL: http://arxiv.org/abs/2305.10010v1
• Date: Wed, 17 May 2023 07:40:12 GMT
• Title: AD-KD: Attribution-Driven Knowledge Distillation for Language Model Compression
• Authors: Siyue Wu, Hongzhan Chen, Xiaojun Quan, Qifan Wang, Rui Wang
• Abstract summary: We present a novel attribution-driven knowledge distillation approach to compress pre-trained language models. To enhance the knowledge transfer of model reasoning and generalization, we explore multi-view attribution distillation on all potential decisions of the teacher.
• Score: 26.474962405945316
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge distillation has attracted a great deal of interest recently to compress pre-trained language models. However, existing knowledge distillation methods suffer from two limitations. First, the student model simply imitates the teacher's behavior while ignoring the underlying reasoning. Second, these methods usually focus on the transfer of sophisticated model-specific knowledge but overlook data-specific knowledge. In this paper, we present a novel attribution-driven knowledge distillation approach, which explores the token-level rationale behind the teacher model based on Integrated Gradients (IG) and transfers attribution knowledge to the student model. To enhance the knowledge transfer of model reasoning and generalization, we further explore multi-view attribution distillation on all potential decisions of the teacher. Comprehensive experiments are conducted with BERT on the GLUE benchmark. The experimental results demonstrate the superior performance of our approach to several state-of-the-art methods.

Related papers

• MLKD-BERT: Multi-level Knowledge Distillation for Pre-trained Language Models [4.404914701832396]
  We propose a novel knowledge distillation method MLKD-BERT to distill multi-level knowledge in teacher-student framework. Our method outperforms state-of-the-art knowledge distillation methods on BERT. In addition, MLKD-BERT can flexibly set student attention head number, allowing for substantial inference time decrease with little performance drop.
  arXiv  Detail & Related papers  (2024-07-03T03:03:30Z)
• Knowledge Distillation via Token-level Relationship Graph [12.356770685214498]
  We propose a novel method called Knowledge Distillation with Token-level Relationship Graph (TRG) By employing TRG, the student model can effectively emulate higher-level semantic information from the teacher model. We conduct experiments to evaluate the effectiveness of the proposed method against several state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-06-20T08:16:37Z)
• Improved Knowledge Distillation for Pre-trained Language Models via Knowledge Selection [35.515135913846386]
  We propose an actor-critic approach to selecting appropriate knowledge to transfer during the process of knowledge distillation. Experimental results on the GLUE datasets show that our method outperforms several strong knowledge distillation baselines significantly.
  arXiv  Detail & Related papers  (2023-02-01T13:40:19Z)
• Exploring Inconsistent Knowledge Distillation for Object Detection with Data Augmentation [66.25738680429463]
  Knowledge Distillation (KD) for object detection aims to train a compact detector by transferring knowledge from a teacher model. We propose inconsistent knowledge distillation (IKD) which aims to distill knowledge inherent in the teacher model's counter-intuitive perceptions. Our method outperforms state-of-the-art KD baselines on one-stage, two-stage and anchor-free object detectors.
  arXiv  Detail & Related papers  (2022-09-20T16:36:28Z)
• Knowledge Distillation Meets Open-Set Semi-Supervised Learning [69.21139647218456]
  We propose a novel em modelname (bfem shortname) method dedicated for distilling representational knowledge semantically from a pretrained teacher to a target student. At the problem level, this establishes an interesting connection between knowledge distillation with open-set semi-supervised learning (SSL) Our shortname outperforms significantly previous state-of-the-art knowledge distillation methods on both coarse object classification and fine face recognition tasks.
  arXiv  Detail & Related papers  (2022-05-13T15:15:27Z)
• On the benefits of knowledge distillation for adversarial robustness [53.41196727255314]
  We show that knowledge distillation can be used directly to boost the performance of state-of-the-art models in adversarial robustness. We present Adversarial Knowledge Distillation (AKD), a new framework to improve a model's robust performance.
  arXiv  Detail & Related papers  (2022-03-14T15:02:13Z)
• Extracting knowledge from features with multilevel abstraction [3.4443503349903124]
  Self-knowledge distillation (SKD) aims at transferring the knowledge from a large teacher model to a small student model. In this paper, we purpose a novel SKD method in a different way from the main stream methods. Experiments and ablation studies show its great effectiveness and generalization on various kinds of tasks.
  arXiv  Detail & Related papers  (2021-12-04T02:25:46Z)
• Revisiting Knowledge Distillation: An Inheritance and Exploration Framework [153.73692961660964]
  Knowledge Distillation (KD) is a popular technique to transfer knowledge from a teacher model to a student model. We propose a novel inheritance and exploration knowledge distillation framework (IE-KD) Our IE-KD framework is generic and can be easily combined with existing distillation or mutual learning methods for training deep neural networks.
  arXiv  Detail & Related papers  (2021-07-01T02:20:56Z)
• Collaborative Teacher-Student Learning via Multiple Knowledge Transfer [79.45526596053728]
  We propose a collaborative teacher-student learning via multiple knowledge transfer (CTSL-MKT) It allows multiple students learn knowledge from both individual instances and instance relations in a collaborative way. The experiments and ablation studies on four image datasets demonstrate that the proposed CTSL-MKT significantly outperforms the state-of-the-art KD methods.
  arXiv  Detail & Related papers  (2021-01-21T07:17:04Z)
• Reinforced Multi-Teacher Selection for Knowledge Distillation [54.72886763796232]
  knowledge distillation is a popular method for model compression. Current methods assign a fixed weight to a teacher model in the whole distillation. Most of the existing methods allocate an equal weight to every teacher model. In this paper, we observe that, due to the complexity of training examples and the differences in student model capability, learning differentially from teacher models can lead to better performance of student models distilled.
  arXiv  Detail & Related papers  (2020-12-11T08:56:39Z)
• DE-RRD: A Knowledge Distillation Framework for Recommender System [16.62204445256007]
  We propose a knowledge distillation framework for recommender system, called DE-RRD. It enables the student model to learn from the latent knowledge encoded in the teacher model as well as from the teacher's predictions. Our experiments show that DE-RRD outperforms the state-of-the-art competitors and achieves comparable or even better performance to that of the teacher model with faster inference time.
  arXiv  Detail & Related papers  (2020-12-08T11:09:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.