mlsanityData to 7 Oct 2026

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

arXiv 2512.16236 ↗First posted 18 Dec 2025cs.IR cs.AIcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 46 of its 78 references that are in the map. First posted on arXiv 18 Dec 2025.

Builds on

46 of 78 references.

References · 46

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34 more
  1. 2020Document Ranking with a Pretrained Sequence-to-Sequence Modelcited by 373
  2. 2023Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agentscited by 356
  3. 2023Fine-Tuning LLaMA for Multi-Stage Text Retrievalcited by 271
  4. 2019Multi-Stage Document Ranking with BERTcited by 200
  5. 2023RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!cited by 151
  6. 2022RankT5: Fine-Tuning T5 for Text Ranking with Ranking Lossescited by 114
  7. 2022Re2G: Retrieve, Rerank, Generatecited by 102
  8. 2023Zero-Shot Listwise Document Reranking with a Large Language Modelcited by 81
  9. 2023RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Modelscited by 69
  10. 2025Rank1: Test-Time Compute for Reranking in Information Retrievalcited by 68
  11. 2018Learning a Deep Listwise Context Model for Ranking Refinementcited by 62
  12. 2023Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-Intensive Taskscited by 62
  13. 2024Don't Forget to Connect! Improving RAG with Graph-based Rerankingcited by 49
  14. 2019SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrievalcited by 48
  15. 2023When Is Multilinguality a Curse? Language Modeling for 250 High- and Low-Resource Languagescited by 32
  16. 2024ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrievalcited by 29
  17. 2024APEER: Automatic Prompt Engineering Enhances Large Language Model Rerankingcited by 28
  18. 2020PiRank: Scalable Learning To Rank via Differentiable Sortingcited by 26
  19. 2022Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrievalcited by 17
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  23. 2021Text-to-Text Multi-view Learning for Passage Re-rankingcited by 14
  24. 2023Rank-without-GPT: Building GPT-Independent Listwise Rerankers on Open-Source Large Language Modelscited by 14
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  27. 2024ReasoningRank: Teaching Student Models to Rank through Reasoning-Based Knowledge Distillationcited by 9
  28. 2024Finetuning Large Language Model for Personalized Rankingcited by 7
  29. 2024An Investigation of Prompt Variations for Zero-shot LLM-based Rankerscited by 7
  30. 2025Distillation and Refinement of Reasoning in Small Language Models for Document Re-rankingcited by 6
  31. 2024InstUPR : Instruction-based Unsupervised Passage Reranking with Large Language Modelscited by 3
  32. 2025ListConRanker: A Contrastive Text Reranker with Listwise Encodingcited by 3
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  34. 2024Passage-specific Prompt Tuning for Passage Reranking in Question Answering with Large Language Modelscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026A Guide to Large Language Models in Modeling and Simulation: From Core Techniques to Critical Challengescited by 2
  2. 2026AquiLLM: An Architecture for Supporting Tacit Knowledge Capture in Research Groupscited by 1
  3. 2026Embedding Models for Stance-Aware Argument Retrievalcited by 0

Similar, not cited

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  1. 2025Graph-Based Re-ranking in Information Retrieval and Beyond: A Survey
  2. 2024Enhancing Q&A Text Retrieval with Ranking Models: Benchmarking, fine-tuning and deploying Rerankers for RAG
  3. 2024JudgeRank: Leveraging Large Language Models for Reasoning-Intensive Reranking
  4. 2025LLM Optimization Unlocks Real-Time Pairwise Reranking
  5. 2025Embedding-Based Context-Aware Reranker
  6. 2025Beyond Sequential Reranking: Reranker-Guided Search Improves Reasoning Intensive Retrieval
  7. 2025GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs
  8. 2025ProRank: Prompt Warmup via Reinforcement Learning for Small Language Models Reranking