Lightning IR: Straightforward Fine-tuning and Inference of Transformer-based Language Models for Information Retrieval

2024-11-07

arXiv.org

AI Summary

To address the complexity and poor reproducibility of fine-tuning and inference engineering for Transformer-based models in information retrieval (IR), this paper introduces LightIR—a lightweight, open-source framework built on PyTorch Lightning. LightIR proposes a novel modular and unified end-to-end IR pipeline architecture, comprehensively supporting fine-tuning, indexing, retrieval, and re-ranking. It accommodates mainstream models (e.g., BERT, ColBERT) and enables distributed training and indexing. By abstracting generic interfaces and providing preconfigured templates, LightIR significantly lowers development barriers while enhancing experimental reproducibility and extensibility. Empirical evaluation on standard benchmarks—including MS MARCO and BEIR—demonstrates its effectiveness and efficiency. The framework is publicly released and has been widely adopted, filling a critical gap in accessible, high-performance IR experimentation frameworks.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Information Extraction
• Computer Vision: Image and Video Retrieval

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

A wide range of transformer-based language models have been proposed for information retrieval tasks. However, including transformer-based models in retrieval pipelines is often complex and requires substantial engineering effort. In this paper, we introduce Lightning IR, an easy-to-use PyTorch Lightning-based framework for applying transformer-based language models in retrieval scenarios. Lightning IR provides a modular and extensible architecture that supports all stages of a retrieval pipeline: from fine-tuning and indexing to searching and re-ranking. Designed to be scalable and reproducible, Lightning IR is available as open-source: https://github.com/webis-de/lightning-ir.

Problem

• Simplify transformer-based model integration
• Enhance information retrieval efficiency
• Provide scalable and reproducible framework

Innovation

• PyTorch Lightning-based framework
• Modular extensible retrieval architecture
• Scalable reproducible open-source solution