@inproceedings{long-etal-2026-grouprank,
title = "{G}roup{R}ank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with {LLM}s",
author = "Long, Meixiu and
Sun, Duolin and
Yang, Dan and
Jiao, Yihan and
Liu, Lei and
Wang, Jiahai and
Hu, Binbin and
Shen, Yue and
Feng, Jie and
Tan, Zhehao and
Wang, Junjie and
Zhong, Lianzhen and
Wang, Jian and
Wei, Peng and
Gu, Jinjie",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1260/",
pages = "25165--25186",
ISBN = "979-8-89176-395-1",
abstract = "Large Language Models (LLMs) have emerged as powerful tools for passage reranking in information retrieval, leveraging their superior reasoning capabilities to address the limitations of conventional models on complex queries. However, current LLM-based reranking paradigms are fundamentally constrained by an efficiency-accuracy trade-off: (1) pointwise methods are efficient but ignore inter-document comparison, yielding suboptimal accuracy; (2) listwise methods capture global context but suffer from context-window constraints and prohibitive inference latency. To address these issues, we propose GroupRank, a novel paradigm that balances flexibility and context awareness. To unlock the full potential of groupwise reranking, we propose an answer-free data synthesis pipeline that fuses local pointwise signals with global listwise rankings. These samples facilitate supervised fine-tuning and reinforcement learning, with the latter guided by a specialized \textit{group-ranking reward} comprising ranking-utility and group-alignment. These complementary components synergistically optimize document ordering and score calibration to reflect intrinsic query-document relevance.Experimental results show GroupRank achieves a state-of-the-art 65.2 NDCG@10 on BRIGHT and surpasses baselines by 2.1 points on R2MED, while delivering a 6.4$\times$ inference speedup. The code is available at https://github.com/AQ-MedAI/Diver/tree/main/Reranker/GroupRank."
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<abstract>Large Language Models (LLMs) have emerged as powerful tools for passage reranking in information retrieval, leveraging their superior reasoning capabilities to address the limitations of conventional models on complex queries. However, current LLM-based reranking paradigms are fundamentally constrained by an efficiency-accuracy trade-off: (1) pointwise methods are efficient but ignore inter-document comparison, yielding suboptimal accuracy; (2) listwise methods capture global context but suffer from context-window constraints and prohibitive inference latency. To address these issues, we propose GroupRank, a novel paradigm that balances flexibility and context awareness. To unlock the full potential of groupwise reranking, we propose an answer-free data synthesis pipeline that fuses local pointwise signals with global listwise rankings. These samples facilitate supervised fine-tuning and reinforcement learning, with the latter guided by a specialized group-ranking reward comprising ranking-utility and group-alignment. These complementary components synergistically optimize document ordering and score calibration to reflect intrinsic query-document relevance.Experimental results show GroupRank achieves a state-of-the-art 65.2 NDCG@10 on BRIGHT and surpasses baselines by 2.1 points on R2MED, while delivering a 6.4\times inference speedup. The code is available at https://github.com/AQ-MedAI/Diver/tree/main/Reranker/GroupRank.</abstract>
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%0 Conference Proceedings
%T GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs
%A Long, Meixiu
%A Sun, Duolin
%A Yang, Dan
%A Jiao, Yihan
%A Liu, Lei
%A Wang, Jiahai
%A Hu, Binbin
%A Shen, Yue
%A Feng, Jie
%A Tan, Zhehao
%A Wang, Junjie
%A Zhong, Lianzhen
%A Wang, Jian
%A Wei, Peng
%A Gu, Jinjie
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F long-etal-2026-grouprank
%X Large Language Models (LLMs) have emerged as powerful tools for passage reranking in information retrieval, leveraging their superior reasoning capabilities to address the limitations of conventional models on complex queries. However, current LLM-based reranking paradigms are fundamentally constrained by an efficiency-accuracy trade-off: (1) pointwise methods are efficient but ignore inter-document comparison, yielding suboptimal accuracy; (2) listwise methods capture global context but suffer from context-window constraints and prohibitive inference latency. To address these issues, we propose GroupRank, a novel paradigm that balances flexibility and context awareness. To unlock the full potential of groupwise reranking, we propose an answer-free data synthesis pipeline that fuses local pointwise signals with global listwise rankings. These samples facilitate supervised fine-tuning and reinforcement learning, with the latter guided by a specialized group-ranking reward comprising ranking-utility and group-alignment. These complementary components synergistically optimize document ordering and score calibration to reflect intrinsic query-document relevance.Experimental results show GroupRank achieves a state-of-the-art 65.2 NDCG@10 on BRIGHT and surpasses baselines by 2.1 points on R2MED, while delivering a 6.4\times inference speedup. The code is available at https://github.com/AQ-MedAI/Diver/tree/main/Reranker/GroupRank.
%U https://aclanthology.org/2026.findings-acl.1260/
%P 25165-25186
Markdown (Informal)
[GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs](https://aclanthology.org/2026.findings-acl.1260/) (Long et al., Findings 2026)
ACL
- Meixiu Long, Duolin Sun, Dan Yang, Yihan Jiao, Lei Liu, Jiahai Wang, Binbin Hu, Yue Shen, Jie Feng, Zhehao Tan, Junjie Wang, Lianzhen Zhong, Jian Wang, Peng Wei, and Jinjie Gu. 2026. GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs. In Findings of the Association for Computational Linguistics: ACL 2026, pages 25165–25186, San Diego, California, United States. Association for Computational Linguistics.