mlsanityData to 7 Oct 2026

rerankers: A Lightweight Python Library to Unify Ranking Methods

arXiv 2408.17344 ↗First posted 30 Aug 2024cs.IR cs.AIcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 20 of its 30 references that are in the map. First posted on arXiv 30 Aug 2024.

Builds on

20 of 30 references.

References · 20

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2023GPT-4 Technical Reportcited by 18,132
  4. 2024Gemma 2: Improving Open Language Models at a Practical Sizecited by 1,928
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  7. 2021ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interactioncited by 464
  8. 2020Document Ranking with a Pretrained Sequence-to-Sequence Modelcited by 373
  9. 2020Pretrained Transformers for Text Ranking: BERT and Beyondcited by 266
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  12. 2019Multi-Stage Document Ranking with BERTcited by 200
8 more
  1. 2021RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-rankingcited by 175
  2. 2023RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!cited by 151
  3. 2023Llama2Vec: Unsupervised Adaptation of Large Language Models for Dense Retrievalcited by 77
  4. 2023RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Modelscited by 69
  5. 2024SPLADE-v3: New baselines for SPLADEcited by 69
  6. 2023Scaling Down, LiTting Up: Efficient Zero-Shot Listwise Reranking with Seq2seq Encoder-Decoder Modelscited by 26
  7. 2024JaColBERTv2.5: Optimising Multi-Vector Retrievers to Create State-of-the-Art Japanese Retrievers with Constrained Resourcescited by 15
  8. 2024ptt5-v2: A Closer Look at Continued Pretraining of T5 Models for the Portuguese Languagecited by 8

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Cited by year

Citing papers per year: 2024: 1; 2025: 7.

8 papers.

  1. 2024JudgeRank: Leveraging Large Language Models for Reasoning-Intensive Rerankingcited by 34
  2. 2025Guiding Retrieval using LLM-based Listwise Rankerscited by 16
  3. 2025Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generationcited by 13
  4. 2025LLM4Ranking: An Easy-to-use Framework of Utilizing Large Language Models for Document Rerankingcited by 12
  5. 2025LRAGE: Legal Retrieval Augmented Generation Evaluation Toolcited by 5
  6. 2025DS@GT at CheckThat! 2025: Evaluating Context and Tokenization Strategies for Numerical Fact Verificationcited by 2
  7. 2025EncouRAGe: Evaluating RAG Local, Fast, and Reliablecited by 2
  8. 2025DS@GT at CheckThat! 2025: Exploring Retrieval and Reranking Pipelines for Scientific Claim Source Retrieval on Social Media Discoursecited by 0