rerankers: A Lightweight Python Library to Unify Ranking Methods (2408.17344v2)

Abstract: This paper presents rerankers, a Python library which provides an easy-to-use interface to the most commonly used re-ranking approaches. Re-ranking is an integral component of many retrieval pipelines; however, there exist numerous approaches to it, relying on different implementation methods. rerankers unifies these methods into a single user-friendly interface, allowing practitioners and researchers alike to explore different methods while only changing a single line of Python code. Moreover ,rerankers ensures that its implementations are done with the fewest dependencies possible, and re-uses the original implementation whenever possible, guaranteeing that our simplified interface results in no performance degradation compared to more complex ones. The full source code and list of supported models are updated regularly and available at https://github.com/answerdotai/rerankers.

• The paper presents a unified interface that simplifies integrating various re-ranking models with minimal dependencies.
• The paper demonstrates competitive performance on standard datasets such as MS Marco, SciFact, and TREC-Covid without performance degradation.
• The paper highlights potential improvements through fine-tuning capabilities, reducing developmental overhead and fostering broader experimentation.

rerankers: A Lightweight Python Library to Unify Ranking Methods

The paper "rerankers: A Lightweight Python Library to Unify Ranking Methods" by Benjamin Clavié introduces a Python library designed to streamline the integration and evaluation of various re-ranking methods in information retrieval systems. The library aims to simplify both implementation and experimentation with multiple re-ranking approaches without incurring performance degradation.

Introduction and Motivation

Two-stage retrieval pipelines, which involve an initial candidate document retrieval followed by a more precise re-ranking phase, are a standard methodology in the field of information retrieval. The primary motivation behind adopting a two-stage process is the need to balance computational efficiency with retrieval accuracy. Initial retrieval methods, such as BM25 or Dense Passage Retrieval (DPR), are designed for speed, while re-ranking models, often based on neural architectures, deliver higher accuracy but at a computational cost. Cross-encoders, sequence-to-sequence models, and late-interaction retrieval models are examples of modern neural re-ranking methodologies utilized to improve performance metrics significantly.

Despite the effectiveness of these combined approaches, the proliferation of various re-ranking techniques presents practical challenges. The diversity in implementation methods and dependencies complicates the process of integrating and evaluating new techniques. This fragmentation can create substantial developmental overhead, deterring researchers and practitioners from exploring newer or less mainstream methods.

Contributions

The rerankers library addresses these challenges by offering a unified, lightweight interface that supports multiple re-ranking methods. The principal features and contributions of rerankers include:

1. Unified Interface: The library encapsulates various re-ranking approaches under a single, cohesive API, enabling users to experiment with different methods by modifying only a single line of code.
2. Minimal Dependencies: rerankers is designed to be minimally intrusive, reusing existing implementations where possible and reducing the dependency footprint.
3. No Performance Degradation: Ensures that the simplified interface does not compromise the performance of the underlying re-ranking methods.

The library is integrated with the HuggingFace transformers ecosystem, facilitating straightforward loading of models from the HuggingFace hub or local storage.

System Overview

The core of rerankers is the Reranker class, which serves as the main entry point for loading models and performing inference. This centralization simplifies the process of swapping between different re-ranking models. The RankedResults object, another fundamental component, offers a standardized way to handle the outputs from various re-ranking algorithms, preserving document metadata and providing utility methods such as top_k() and direct score retrieval.

Performance Evaluation

The library's efficiency and effectiveness were validated through top-1000 re-ranking evaluations on three commonly used datasets (subsets of MS Marco, SciFact, and TREC-Covid). For most models included in rerankers, the performance was on par with existing implementations, confirming that the library's unified interface does not degrade the underlying model effectiveness.

A notable exception was observed with RankGPT, where performance varied more significantly, likely due to the inherent variability in API-only models. This further highlights the challenges in reproducibility for some advanced and proprietary LLM-based methods.

Implications and Future Work

rerankers significantly lowers the barriers to entry for experimenting with various re-ranking techniques, which has implications for both theoretical research and practical applications. By reducing the overhead associated with adopting new methods, the library can foster innovation and broader experimentation within the community.

Future development of rerankers aims to incorporate fine-tuning capabilities, allowing users to train and adapt models within the same unified interface. This would further enhance the library's utility, making it a comprehensive tool for both inference and training tasks in information retrieval.

The work also underscores the potential for re-ranking methods to contribute to knowledge distillation processes, enhancing the performance of first-stage retrieval models. By simplifying access to diverse re-ranking approaches, rerankers can support the development and deployment of more effective retrieval pipelines.

In summary, "rerankers: A Lightweight Python Library to Unify Ranking Methods" presents a valuable tool for the information retrieval community, addressing critical practical and developmental challenges through its well-designed, modular, and high-performance architecture.