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    <abstract>Existing approaches typically rely on large-scale fine-tuning to adapt LLMs for information reranking tasks, which is computationally expensive. In this work, we demonstrate that modern LLMs can be effectively adapted using only minimal, high-quality supervision. To enable this, we design LIMRANK-SYNTHESIZER, a reusable and open-source pipeline for generating diverse, challenging, and realistic reranking examples. Using this synthetic data, we fine-tune our reranker model, LIMRANK. We evaluate LIMRANK on two challenging benchmarks, i.e., BRIGHT for reasoning-intensive retrieval and FollowIR for instruction-following retrieval. Our experiments demonstrate that LIMRANK achieves competitive performance, while being trained on less than 5% of the data typically used in prior work. Further ablation studies demonstrate the effectiveness of LIMRANK-SYNTHESIZER and the strong generalization capabilities of LIMRANK across downstream tasks, including scientific literature search and retrieval-augmented generation for knowledge-intensive problem solving.</abstract>
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%0 Conference Proceedings
%T LimRank: Less is More for Reasoning-Intensive Information Reranking
%A Song, Tingyu
%A Zhao, Yilun
%A Zhang, Siyue
%A Zhao, Chen
%A Cohan, Arman
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F song-etal-2025-limrank
%X Existing approaches typically rely on large-scale fine-tuning to adapt LLMs for information reranking tasks, which is computationally expensive. In this work, we demonstrate that modern LLMs can be effectively adapted using only minimal, high-quality supervision. To enable this, we design LIMRANK-SYNTHESIZER, a reusable and open-source pipeline for generating diverse, challenging, and realistic reranking examples. Using this synthetic data, we fine-tune our reranker model, LIMRANK. We evaluate LIMRANK on two challenging benchmarks, i.e., BRIGHT for reasoning-intensive retrieval and FollowIR for instruction-following retrieval. Our experiments demonstrate that LIMRANK achieves competitive performance, while being trained on less than 5% of the data typically used in prior work. Further ablation studies demonstrate the effectiveness of LIMRANK-SYNTHESIZER and the strong generalization capabilities of LIMRANK across downstream tasks, including scientific literature search and retrieval-augmented generation for knowledge-intensive problem solving.
%R 10.18653/v1/2025.emnlp-main.1041
%U https://aclanthology.org/2025.emnlp-main.1041/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1041
%P 20625-20639

Markdown (Informal)

[LimRank: Less is More for Reasoning-Intensive Information Reranking](https://aclanthology.org/2025.emnlp-main.1041/) (Song et al., EMNLP 2025)

• LimRank: Less is More for Reasoning-Intensive Information Reranking (Song et al., EMNLP 2025)

ACL

• Tingyu Song, Yilun Zhao, Siyue Zhang, Chen Zhao, and Arman Cohan. 2025. LimRank: Less is More for Reasoning-Intensive Information Reranking. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 20625–20639, Suzhou, China. Association for Computational Linguistics.