ProRank: Prompt Warmup via Reinforcement Learning for Small Language Models Reranking (2026.findings-acl)
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| Challenge: | Recent Large Language Models (LLMs) have demonstrated remarkable performance in document reranking tasks. |
| Approach: | They propose a two-stage training approach for document reranking using reinforcement learning and fine-grained score learning. |
| Outcome: | The proposed approach outperforms open-source and proprietary reranking models on BEIR benchmark. |
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EcoRank: Budget-Constrained Text Re-ranking Using Large Language Models (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated dominant performance in text re-ranking. |
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| Challenge: | Existing listwise LLMs lack efficiency as they provide ranking output in the form of a generated ordered sequence of candidate passage identifiers. |
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GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs (2026.findings-acl)
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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, Jinjie Gu
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Are LLMs Reliable Rankers? Rank Manipulation via Two-Stage Token Optimization (2026.acl-long)
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| Challenge: | Large language models (LLMs) are increasingly used as rerankers, but their ranking behavior can be steered by small, natural-sounding prompts. |
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LimRank: Less is More for Reasoning-Intensive Information Reranking (2025.emnlp-main)
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| Challenge: | Existing approaches to rerank information require large-scale fine-tuning, which is computationally expensive. |
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Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting (2024.findings-naacl)
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Zhen Qin, Rolf Jagerman, Kai Hui, Honglei Zhuang, Junru Wu, Le Yan, Jiaming Shen, Tianqi Liu, Jialu Liu, Donald Metzler, Xuanhui Wang, Michael Bendersky
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Shengyu Mao, Yong Jiang, Boli Chen, Xiao Li, Peng Wang, Xinyu Wang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang
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PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) excel in zero-shot document ranking tasks. |
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Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples! (2023.findings-emnlp)
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| Challenge: | Large Language Models have made remarkable strides in various tasks, but whether they are competitive few-shot solvers remains an open question. |
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