LEAD: Liberal Feature-based Distillation for Dense Retrieval (2212.05225v2)

Abstract: Knowledge distillation is often used to transfer knowledge from a strong teacher model to a relatively weak student model. Traditional methods include response-based methods and feature-based methods. Response-based methods are widely used but suffer from lower upper limits of performance due to their ignorance of intermediate signals, while feature-based methods have constraints on vocabularies, tokenizers and model architectures. In this paper, we propose a liberal feature-based distillation method (LEAD). LEAD aligns the distribution between the intermediate layers of teacher model and student model, which is effective, extendable, portable and has no requirements on vocabularies, tokenizers, or model architectures. Extensive experiments show the effectiveness of LEAD on widely-used benchmarks, including MS MARCO Passage Ranking, TREC 2019 DL Track, MS MARCO Document Ranking and TREC 2020 DL Track. Our code is available in https://github.com/microsoft/SimXNS/tree/main/LEAD.

• The paper introduces LEAD, a novel method that aligns intermediate layers to transfer knowledge from teacher to student models without strict architectural constraints.
• It employs layer-wise alignment and adaptive re-weighting techniques to optimize feature distributions and improve retrieval metrics like MRR@10 and MAP@1k.
• Experimental results on benchmarks such as MS MACRO and TREC demonstrate that LEAD outperforms conventional response-based and feature-based distillation methods.

Analyzing LEAD: A Liberal Feature-based Distillation Approach for Dense Retrieval

The paper "LEAD: Liberal Feature-based Distillation for Dense Retrieval" introduces a novel methodology for enhancing dense retrieval systems through a knowledge distillation framework. The research addresses key limitations in traditional response-based and feature-based distillation methods, proposing the LEAD mechanism to optimize the transfer of knowledge from more complex, pre-trained models to more efficient models without the constraints typically encountered in traditional distillation processes.

Background and Challenges

Dense retrieval has emerged as a powerful approach for information retrieval, leveraging pre-trained LLMs such as BERT and its variants to encode queries and passages into dense vectors. Despite their success, these models often face trade-offs between efficiency and effectiveness, primarily due to their substantial computational demands. Knowledge distillation has traditionally been employed to mitigate these concerns by transferring knowledge from a larger teacher model to a smaller student model. However, conventional response-based distillation often ignores intermediate signals crucial to understanding model behavior, while feature-based methods impose constraints on the models concerning vocabulary, tokenizers, and architecture.

LEAD Methodology

The LEAD approach provides a liberal feature-based distillation framework designed to align the distribution of intermediate layers between teacher and student models. Specifically, LEAD utilizes the [CLS] token embedding vectors to compute similarity distributions across passages without relying on shared vocabularies or tokenizer definitions. This enables a more flexible and architecture-independent distillation process.

Key innovations within LEAD include:

• Layer-wise Alignment: LEAD allows random selection of layers from both teacher and student models to participate in distillation, preserving the order of layers. This choice aims for balanced knowledge transfer without strict architectural constraints.
• Layer Re-weighting Technique: Introduced to adaptively allocate weights to teacher layers based on their informative value, enhancing the effectiveness of the distillation process.
• Joint Training of Teacher and Student: Contrasting with static teacher configurations commonly found in current practices, LEAD enables concurrent optimization of both teacher and student models, allowing them to co-evolve and enhance each other’s performance.

Empirical Results

The research evaluates LEAD across benchmarks such as MS MACRO and TREC, demonstrating that LEAD outperforms traditional response-based and feature-based methods notably. Experimental setups with configurations like "12CB teacher to 6DE student" confirm that LEAD maintains flexibility and superior performance over diverse model architectures like DE and CB.

LEAD's superiority is attributed to:

• Its ability to distill finer-grained knowledge preserved in intermediate layer alignments.
• Empirical evidence of improved performance metrics (such as MRR@10 and MAP@1k) in dense retrieval scenarios when compared to competitive response-based methods like RD and feature-based methods like FD.

Implications and Future Directions

LEAD presents significant advancements in enabling effective dense retrieval on more efficient terms, reinforcing the potential to increase the applicability of dense retrieval models in real-world scenarios where computational resources are constrained. It opens avenues for future exploration in integrating LEAD with other preprocessing techniques such as curriculum learning and data augmentation, potentially exceeding current benchmark performances.

Future advancements of this approach could explore refining layer selection strategies or expanding its application to other neural networks beyond NLP contexts, testing the robustness and adaptability of LEAD in an ever-growing landscape of AI applications.