Innovative Distillation Method by Microsoft & Xiamen U Advances Dense Retrieval
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Chapter 1: Introduction to Knowledge Distillation
Knowledge distillation is a well-established technique used to transfer expertise from a more complex teacher model to a simpler student model. One might think that a more advanced teacher would inherently lead to a more capable student; however, this is not always the case, particularly when there's a significant disparity in their capabilities. As researchers from Xiamen University and Microsoft illustrate, "a university professor may not be the best fit to instruct a kindergarten student."
Section 1.1: Overview of PROD
In their recent publication, "Progressive Distillation for Dense Retrieval," the collaborative research team from Xiamen University and Microsoft introduces PROD, a novel progressive distillation technique specifically designed for dense retrieval tasks, which involve matching queries with documents. PROD has achieved state-of-the-art performance across five established benchmark datasets.
Subsection 1.1.1: Mechanisms of PROD
PROD focuses on gradually closing the performance gap between the teacher model and the target student model through two key sequential mechanisms:
- Teacher Progressive Distillation (TPD): This component progressively enhances the teacher's capabilities, enabling students to learn in stages.
- Data Progressive Distillation (DPD): Initially, students receive access to all available data, after which the focus shifts to the samples where the student struggles. This approach is akin to a tutor's method, ensuring that the knowledge imparted is neither too simplistic nor overly challenging. Additionally, regularization loss is implemented at each progressive step to mitigate the risk of catastrophic forgetting.
Section 1.2: Implementation of PROD
The PROD framework utilizes three distinct teacher models, each with varying levels of proficiency: a 12-layer Dual Encoder (DE), a 12-layer Cross-Encoder (CE), and a 24-layer CE. This layered approach aims to enhance the capabilities of a 6-layer DE student model incrementally.
Chapter 2: Empirical Validation of PROD
The research team conducted extensive experiments on five prominent benchmark datasets: MS MARCO Passage, TREC Passage 19, TREC Document 19, MS MARCO Document, and Natural Questions. The results demonstrated that PROD achieved state-of-the-art outcomes for dense retrieval across all datasets.
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In summary, this study confirms the efficacy of the proposed PROD distillation method as a valuable approach to dense retrieval. The researchers are optimistic that their findings will encourage further exploration in this field. The paper "Progressive Distillation for Dense Retrieval" is available on arXiv.
Author: Hecate He | Editor: Michael Sarazen
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