Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation (2010.02666v2)

Abstract: Retrieval and ranking models are the backbone of many applications such as web search, open domain QA, or text-based recommender systems. The latency of neural ranking models at query time is largely dependent on the architecture and deliberate choices by their designers to trade-off effectiveness for higher efficiency. This focus on low query latency of a rising number of efficient ranking architectures make them feasible for production deployment. In machine learning an increasingly common approach to close the effectiveness gap of more efficient models is to apply knowledge distillation from a large teacher model to a smaller student model. We find that different ranking architectures tend to produce output scores in different magnitudes. Based on this finding, we propose a cross-architecture training procedure with a margin focused loss (Margin-MSE), that adapts knowledge distillation to the varying score output distributions of different BERT and non-BERT passage ranking architectures. We apply the teachable information as additional fine-grained labels to existing training triples of the MSMARCO-Passage collection. We evaluate our procedure of distilling knowledge from state-of-the-art concatenated BERT models to four different efficient architectures (TK, ColBERT, PreTT, and a BERT CLS dot product model). We show that across our evaluated architectures our Margin-MSE knowledge distillation significantly improves re-ranking effectiveness without compromising their efficiency. Additionally, we show our general distillation method to improve nearest neighbor based index retrieval with the BERT dot product model, offering competitive results with specialized and much more costly training methods. To benefit the community, we publish the teacher-score training files in a ready-to-use package.

Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation

The paper "Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation" by Sebastian Hofstätter et al. introduces a notable advancement in the field of Information Retrieval (IR) by focusing on the trade-offs between effectiveness and efficiency in neural ranking models. The authors propose a method for augmenting the effectiveness of low-latency neural ranking architectures through novel cross-architecture knowledge distillation techniques derived from state-of-the-art BERT-based models.

The research emphasizes that neural ranking models are core components of several critical applications like web search and text-based recommender systems. The potential of neural models is often restricted due to high query latency associated with more accurate but computationally expensive models, such as those utilizing the full BERT architecture—BERT$_{CAT}$, which processes concatenated query-passage inputs during scoring.

Key Contributions

1. Cross-Architecture Knowledge Distillation: The paper proposes a cross-architecture training paradigm that leverages a margin-focused loss known as Margin-MSE. This approach adapts knowledge distillation to accommodate disparities in the output score distributions across different ranking architectures. This technique aligns the relative margins between relevant and non-relevant samples across models, utilizing teacher models to guide more efficient student models without strict adherence to absolute score replication.
2. Models Evaluated: The authors evaluate various efficient architectures, including TK, ColBERT, PreTT, and a BERT-CLS dot product (BERT$_{DOT}$) model, using knowledge distilled from concatenated BERT models. They conduct comprehensive experiments on the MSMARCO-Passage dataset to assess how well these student models generalize using refined guidance from more complex architectures.
3. Significant Findings: The paper finds that their Margin-MSE loss significantly improves the re-ranking effectiveness of more efficient architectures while preserving their inherent latency advantages. This work shows that student models trained under cross-architecture distillation, especially with a teacher ensemble, achieve enhanced effectiveness across key metrics compared to isolated training or single-teacher scenarios. Notably, the ensembled teacher models outperform single-instance teachers on various metrics, suggesting potential in leveraging diverse learned patterns.
4. Implications for Dense Retrieval: The paper extends its findings to dense retrieval contexts, showcasing competitive performance against more resource-intensive methods. This demonstrates the adaptability and efficacy of the proposed knowledge distillation approach in reducing computational demand without sacrificing ranking accuracy.
5. Community Contribution: By releasing trained teacher score files, the authors aim to provide the IR community with valuable resources for further exploration and practical implementation of their cross-architecture distillation techniques.

Implications and Speculation on Future AI Developments

This research underscores the potential of knowledge distillation not only as a narrowing agent for effectiveness-efficiency gaps but also as a form of model compression that democratizes the application of powerful AI models in real-world settings with constrained computational resources. Looking ahead, innovations in cross-architecture knowledge transfer could lead to more widely applicable and efficient architectures capable of sustaining the accuracy of large-scale models without inheriting their computational burdens. This work sets a precedent for future AI development where the decoupling of complexity and performance could reshape the landscape for deploying highly capable models in an increasingly resource-conscious world. Towards this end, research may evolve to include more nuanced forms of transfer learning and distilled learning that incorporate a diversity of models and tasks, extending the applicability of these foundational techniques beyond current IR scenarios.

The authors' contribution through this paper is a step toward evolving the nexus of neural network research that innovatively tackles foundational challenges of efficiency and scalability—critical considerations for the continuous integration of AI into diverse, practical domains.