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The Evolution of Reranking Models in Information Retrieval: From Heuristic Methods to Large Language Models

Tejul Pandit, Sakshi Mahendru, Meet Raval, Dhvani Upadhyay

TL;DR

This paper surveys the evolution of reranking in information retrieval, tracing the path from early learning-to-rank methods to deep neural rerankers and the emergence of large language models within RAG pipelines. It systematically reviews transformer-based architectures (BERT and T5), efficient designs like ColBERT and late-interaction strategies, and alternate architectures that model inter-document relations. It then examines efficiency through knowledge distillation, including rationale-aware approaches, and explores the rising role of LLM-based rerankers with prompting, fine-tuning, and open-source alternatives. The work highlights practical trade-offs, such as computational overhead and data requirements, and suggests future directions that integrate diverse approaches for efficient, accurate, and interpretable reranking in real-world IR systems.

Abstract

Reranking is a critical stage in contemporary information retrieval (IR) systems, improving the relevance of the user-presented final results by honing initial candidate sets. This paper is a thorough guide to examine the changing reranker landscape and offer a clear view of the advancements made in reranking methods. We present a comprehensive survey of reranking models employed in IR, particularly within modern Retrieval Augmented Generation (RAG) pipelines, where retrieved documents notably influence output quality. We embark on a chronological journey through the historical trajectory of reranking techniques, starting with foundational approaches, before exploring the wide range of sophisticated neural network architectures such as cross-encoders, sequence-generation models like T5, and Graph Neural Networks (GNNs) utilized for structural information. Recognizing the computational cost of advancing neural rerankers, we analyze techniques for enhancing efficiency, notably knowledge distillation for creating competitive, lighter alternatives. Furthermore, we map the emerging territory of integrating Large Language Models (LLMs) in reranking, examining novel prompting strategies and fine-tuning tactics. This survey seeks to elucidate the fundamental ideas, relative effectiveness, computational features, and real-world trade-offs of various reranking strategies. The survey provides a structured synthesis of the diverse reranking paradigms, highlighting their underlying principles and comparative strengths and weaknesses.

The Evolution of Reranking Models in Information Retrieval: From Heuristic Methods to Large Language Models

TL;DR

This paper surveys the evolution of reranking in information retrieval, tracing the path from early learning-to-rank methods to deep neural rerankers and the emergence of large language models within RAG pipelines. It systematically reviews transformer-based architectures (BERT and T5), efficient designs like ColBERT and late-interaction strategies, and alternate architectures that model inter-document relations. It then examines efficiency through knowledge distillation, including rationale-aware approaches, and explores the rising role of LLM-based rerankers with prompting, fine-tuning, and open-source alternatives. The work highlights practical trade-offs, such as computational overhead and data requirements, and suggests future directions that integrate diverse approaches for efficient, accurate, and interpretable reranking in real-world IR systems.

Abstract

Reranking is a critical stage in contemporary information retrieval (IR) systems, improving the relevance of the user-presented final results by honing initial candidate sets. This paper is a thorough guide to examine the changing reranker landscape and offer a clear view of the advancements made in reranking methods. We present a comprehensive survey of reranking models employed in IR, particularly within modern Retrieval Augmented Generation (RAG) pipelines, where retrieved documents notably influence output quality. We embark on a chronological journey through the historical trajectory of reranking techniques, starting with foundational approaches, before exploring the wide range of sophisticated neural network architectures such as cross-encoders, sequence-generation models like T5, and Graph Neural Networks (GNNs) utilized for structural information. Recognizing the computational cost of advancing neural rerankers, we analyze techniques for enhancing efficiency, notably knowledge distillation for creating competitive, lighter alternatives. Furthermore, we map the emerging territory of integrating Large Language Models (LLMs) in reranking, examining novel prompting strategies and fine-tuning tactics. This survey seeks to elucidate the fundamental ideas, relative effectiveness, computational features, and real-world trade-offs of various reranking strategies. The survey provides a structured synthesis of the diverse reranking paradigms, highlighting their underlying principles and comparative strengths and weaknesses.

Paper Structure

This paper contains 13 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Background
  3. Learning to Rank in Reranking Models
  4. Deep Learning Techniques for Reranking
  5. Transformer-based Rerankers (BERT and T5)
  6. BERT-like Cross-Encoders
  7. T5-based Sequence-to-Sequence Rerankers
  8. Advanced Interaction and Alternative Architectures
  9. Efficiency Techniques: Knowledge Distillation
  10. Distillation Training Strategies for Reasoning-Aware Reranking
  11. Comparison & Observations
  12. LLM Based Rerankers
  13. Conclusion

Figures (1)

  • Figure 1: RAG approach highlighting a post-retrieval step of Reranking documents.