Matryoshka Re-Ranker: A Flexible Re-Ranking Architecture With Configurable Depth and Width (2501.16302v1)

Abstract: LLMs provide powerful foundations to perform fine-grained text re-ranking. However, they are often prohibitive in reality due to constraints on computation bandwidth. In this work, we propose a \textbf{flexible} architecture called \textbf{Matroyshka Re-Ranker}, which is designed to facilitate \textbf{runtime customization} of model layers and sequence lengths at each layer based on users' configurations. Consequently, the LLM-based re-rankers can be made applicable across various real-world situations. The increased flexibility may come at the cost of precision loss. To address this problem, we introduce a suite of techniques to optimize the performance. First, we propose \textbf{cascaded self-distillation}, where each sub-architecture learns to preserve a precise re-ranking performance from its super components, whose predictions can be exploited as smooth and informative teacher signals. Second, we design a \textbf{factorized compensation mechanism}, where two collaborative Low-Rank Adaptation modules, vertical and horizontal, are jointly employed to compensate for the precision loss resulted from arbitrary combinations of layer and sequence compression. We perform comprehensive experiments based on the passage and document retrieval datasets from MSMARCO, along with all public datasets from BEIR benchmark. In our experiments, Matryoshka Re-Ranker substantially outperforms the existing methods, while effectively preserving its superior performance across various forms of compression and different application scenarios.

• The paper presents a flexible re-ranking architecture that dynamically customizes LLM depth and width to reduce computational overhead.
• It employs cascaded self-distillation to allow smaller nested models to inherit the performance of larger counterparts without extensive fine-tuning.
• Factorized compensation via dual LoRA modules mitigates precision loss, achieving state-of-the-art performance on MSMARCO and BEIR benchmarks.

An Overview of "Matryoshka Re-Ranker: A Flexible Re-Ranking Architecture With Configurable Depth and Width"

The paper introduces the "Matryoshka Re-Ranker," a novel architecture designed to enhance the process of re-ranking in text retrieval applications using LLMs. The primary aim is to tackle the computational challenges presented by LLMs, making them more adaptable and efficient for a variety of real-world scenarios without significant loss in precision.

Core Contributions

The Matryoshka Re-Ranker is primarily notable for its flexibility, allowing dynamic customization of LLM architectures in terms of both their depth (number of layers) and width (sequence length). This design paradigm is inspired by the functionality and concept of Russian nesting dolls, known as Matryoshka dolls, which nest within one another, representing the configurable nature of the model.

Here are the core contributions of the Matryoshka Re-Ranker framework:

1. Flexible Architecture for Depth and Width Customization: Users can adjust the model's parameters to suit varying computational constraints effectively. This is particularly useful for deployment in environments with differing resource availability. The architecture supports on-the-fly adjustments, eschewing the need for repetitive fine-tuning processes common in traditional compressed models.
2. Cascaded Self-Distillation: The paper introduces a training regimen that exploits self-distillation cascades. This method allows smaller, nested models within the full-scale architecture to inherit the predictive precision of their larger counterparts. It iteratively samples sub-structures, leveraging the informative output of broader models to train narrower configurations.
3. Factorized Compensation Mechanism: To mitigate precision loss due to structured compression, a dual pathway approach involving vertical and horizontal Low-Rank Adaptation (LoRA) modules is proposed. This factorization enables precise compensation for both depth and width reductions across varied architectural configurations.

Experimental Findings

The Matryoshka Re-Ranker's efficacy is corroborated through comprehensive experimentation on distinct benchmarks: MSMARCO, BEIR, and various public datasets. The outcomes are significant:

• Performance: When applied to MSMARCO and BEIR datasets, the Matryoshka Re-Ranker showed state-of-the-art performance, effectively maintaining high precision even with reduced computational requirements. Notably, the model outperformed existing baseline re-rankers, including those relying on popular LLMs like fine-tuned Llama and commercially successful models like GPT-4.
• Flexibility and Adaptability: Experiments demonstrate the Matryoshka Re-Ranker's robustness to varying degrees of height and width compression. This is pivotal for applications requiring real-time efficiency and indicates potential cost savings without compromising retrieval precision.

Implications and Future Directions

The Matryoshka Re-Ranker stands as a viable solution to the trade-offs between model size and performance in LLM-based re-ranking tasks. It aligns well with the growing necessity for adaptable deep learning frameworks capable of operating efficiently across diverse hardware and production environments. Moreover, its adaptable architecture suggests a broader utilization spectrum, where context-aware recalibration is vital.

Future developments might explore further integration with emerging LLM paradigms and other adaptation mechanisms. Additionally, real-world deployments could illuminate further areas for refinement, such as enhanced layer-pruning strategies and broader versatility in model adaptations, contributing to the ongoing evolution of efficient machine learning strategies.

Overall, this work expands the boundaries of efficient LLM utilization in information retrieval, delivering an innovative framework that balances computational pragmatics with precision efficacy.