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Text reranking models are a crucial component in modern systems like Retrieval-Augmented Generation, tasked with selecting the most relevant documents prior to generation.
Overview of the TREC 2019 deep learning track
Craswell, N.; Mitra, B.; Yilmaz, E.; Campos, D.; and Voorhees, E. M. 2020 · 2003
Earlier work this paper cites.
MS MARCO: A human generated machine reading comprehension dataset
Bajaj, P.; Campos, D.; Craswell, N.; Deng, L.; Gao, J.; Liu, X.; Majumder, R.; McNamara, A.; Mitra, B.; Nguyen, T.; et al. 2016 · 2016
Earlier work this paper cites.
Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering
Lee, J.; Yun, S.; Kim, H.; Ko, M.; and Kang, J. 2018 · 2018
Earlier work this paper cites.
TPR: Text-aware preference ranking for recommender systems
Chuang, Y.-N.; Chen, C.-M.; Wang, C.-J.; Tsai, M.-F.; Fang, Y.; and Lim, E.-P. 2020 · 2020
Earlier work this paper cites.
Overview of the TREC 2020 deep learning track
Craswell, N.; Mitra, B.; Yilmaz, E.; and Campos, D. 2021 · 2020
Earlier work this paper cites.
Rethink training of BERT rerankers in multi-stage retrieval pipeline
Gao, L.; Dai, Z.; and Callan, J. 2021 · 2021
Earlier work this paper cites.
Pyserini: A Python toolkit for reproducible information retrieval research with sparse and dense representations
Lin, J.; Ma, X.; Lin, S.-C.; Yang, J.-H.; Pradeep, R.; and Nogueira, R. 2021 · 2021
Earlier work this paper cites.
BEIR: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models
Thakur, N.; Reimers, N.; Rücklé, A.; Srivastava, A.; and Gurevych, I. 2021 · 2021
Earlier work this paper cites.
Lora: Low-rank adaptation of large language models
Hu, E. J.; Shen, Y.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; Chen, W.; et al. 2022 · 2022
Earlier work this paper cites.
Holistic evaluation of language models
Liang, P.; Bommasani, R.; Lee, T.; Tsipras, D.; Soylu, D.; Yasunaga, M.; Zhang, Y.; Narayanan, D.; Wu, Y.; Kumar, A.; et al. 2022 · 2022
Earlier work this paper cites.
Pretrained transformers for text ranking: Bert and beyond
Lin, J.; Nogueira, R.; and Yates, A. 2022 · 2022
Earlier work this paper cites.
HLATR: enhance multi-stage text retrieval with hybrid list aware transformer reranking
Zhang, Y.; Long, D.; Xu, G.; and Xie, P. 2022 · 2022
Earlier work this paper cites.
Zero-shot listwise document reranking with a large language model
Ma, X.; Zhang, X.; Pradeep, R.; and Lin, J. 2023 · 2023
Earlier work this paper cites.
Is ChatGPT good at search? investigating large language models as re-ranking agents
Sun, W.; Yan, L.; Ma, X.; Wang, S.; Ren, P.; Chen, Z.; Yin, D.; and Ren, Z. 2023 · 2023
Earlier work this paper cites.
Empirical evaluation of ChatGPT on requirements information retrieval under zero-shot setting
Zhang, J.; Chen, Y.; Liu, C.; Niu, N.; and Wang, Y. 2023 · 2023
Cited alongside, same era.
Gupta, S.; Ranjan, R.; and Singh, S. N. 2024 · 2024
Cited alongside, same era.
Understanding the planning of LLM agents: A survey
Huang, X.; Liu, W.; Chen, X.; Wang, X.; Wang, H.; Lian, D.; Wang, Y.; Tang, R.; and Chen, E. 2024 · 2024
Cited alongside, same era.
A survey on LLM-based multi-agent systems: workflow, infrastructure, and challenges
Li, X.; Wang, S.; Zeng, S.; Wu, Y.; and Yang, Y. 2024 · 2024
Cited alongside, same era.
Information retrieval meets large language models
Liu, Z.; Zhou, Y.; Zhu, Y.; Lian, J.; Li, C.; Dou, Z.; Lian, D.; and Nie, J.-Y. 2024 · 2024
Retrieval-augmented generation for natural language processing: A survey
Wu, S.; Xiong, Y.; Cui, Y.; Wu, H.; Chen, C.; Yuan, Y.; Huang, L.; Liu, X.; Kuo, T.-W.; Guan, N.; et al. 2024 · 2024
Later among the works it cites.
A Two-Stage Adaptation of Large Language Models for Text Ranking
Zhang, L.; Zhang, Y.; Long, D.; Xie, P.; Zhang, M.; and Zhang, M. 2024 · 2024
Later among the works it cites.
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Zheng, Y.; Zhang, R.; Zhang, J.; Ye, Y.; Luo, Z.; Feng, Z.; and Ma, Y. 2024 · 2024
Later among the works it cites.
A setwise approach for effective and highly efficient zero-shot ranking with large language models
Zhuang, S.; Zhuang, H.; Koopman, B.; and Zuccon, G. 2024 · 2024
Later among the works it cites.
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
DeepSeek AI. 2025 · 2025
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Cited alongside, same era.
Fine-tuning llama for multi-stage text retrieval
Ma, X.; Wang, L.; Yang, N.; Wei, F.; and Lin, J. 2024 · 2024
Cited alongside, same era.
JudgeRank: Leveraging Large Language Models for Reasoning-Intensive Reranking
Niu, T.; Joty, S.; Liu, Y.; Xiong, C.; Zhou, Y.; and Yavuz, S. 2024 · 2024
Cited alongside, same era.
OpenAI. 2024 · 2024
Cited alongside, same era.
Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting
Qin, Z.; Jagerman, R.; Hui, K.; Zhuang, H.; Wu, J.; Yan, L.; Shen, J.; Liu, T.; Liu, J.; Metzler, D.; et al. 2024 · 2024
Cited alongside, same era.
DeepSeekMath: Pushing the limits of mathematical reasoning in open language models
Shao, Z.; Wang, P.; Zhu, Q.; Xu, R.; Song, J.; Bi, X.; Zhang, H.; Zhang, M.; Li, Y.; Wu, Y.; et al. 2024 · 2024
Cited alongside, same era.
HybridFlow: A Flexible and Efficient RLHF Framework
Sheng, G.; Zhang, C.; Ye, Z.; Wu, X.; Zhang, W.; Zhang, R.; Peng, Y.; Lin, H.; and Wu, C. 2024 · 2024
Cited alongside, same era.
BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval
Su, H.; Yen, H.; Xia, M.; Shi, W.; Muennighoff, N.; Wang, H.-y.; Liu, H.; Shi, Q.; Siegel, Z. S.; Tang, M.; Sun, R.; Yoon, J.; Arik, S. O.; Chen, D.; and Yu, T. 2024 · 2024
Cited alongside, same era.
Closest in time.
Llm4rerank: Llm-based auto-reranking framework for recommendations
Gao, J.; Chen, B.; Zhao, X.; Liu, W.; Li, X.; Wang, Y.; Wang, W.; Guo, H.; and Tang, R. 2025 · 2025
Closest in time.
Rank-R1-32B-v0.2
ielabgroup. 2025 · 2025
Closest in time.
XRR2: Expand → \rightarrow Retrieve → \rightarrow Rerank → \rightarrow Rerank - simple method with strong results on BRIGHT benchmark
jataware. 2025 · 2025
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ListConRanker: A Contrastive Text Reranker with Listwise Encoding
Liu, J.; Ma, Y.; Zhao, R.; Zheng, J.; Ma, Q.; and Kang, Y. 2025 · 2025
Closest in time.
ReasonIR: Training Retrievers for Reasoning Tasks
Shao, R.; Qiao, R.; Kishore, V.; Muennighoff, N.; Lin, X. V.; Rus, D.; Low, B. K. H.; Min, S.; Yih, W.-t.; Koh, P. W.; et al. 2025 · 2025
Closest in time.
BRIGHT Benchmark Online Website
Su, H.; Yen, H.; Xia, M.; Shi, W.; Muennighoff, N.; Wang, H.-y.; Liu, H.; Shi, Q.; Siegel, Z. S.; Tang, M.; Sun, R.; Yoon, J.; Arik, S. O.; Chen, D.; and Yu, T. 2025 · 2025
Closest in time.
FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions
Weller, O.; Chang, B.; MacAvaney, S.; Lo, K.; Cohan, A.; Van Durme, B.; Lawrie, D.; and Soldaini, L. 2025a · 2025
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Rerank: Reasoning Re-ranking Agent via Reinforcement Learning
Zhang, L.; Wang, B.; Qiu, X.; Reddy, S.; and Agrawal, A. 2025 · 2025
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Rank-R1: Enhancing reasoning in llm-based document rerankers via reinforcement learning
Zhuang, S.; Ma, X.; Koopman, B.; Lin, J.; and Zuccon, G. 2025 · 2025
Closest in time.