参数高效微调（Parameter Efficient Fine-Tuning, PEFT）方法已被广泛应用于大语言模型（Large Language Models, LLMs）中，无需微调整个LLM即可提升下游任务性能。现有研究已证明PEFT可有效用于排序任务的LLM微调并取得了令人信服的效果，但仍存在一些局限，包括针对不同文档学习到的提示固定、对特定任务过拟合以及适配能力较低等问题。本文提出一种面向文本重排序的查询依赖参数高效微调（Q-PEFT）方法，将真实查询的信息注入LLM，从而使LLM从输入文档生成真实查询的过程更简单。具体而言，我们利用查询从拼接后的文档中提取前k个token作为上下文线索。我们进一步用多头注意力层替代检索机制来增强Q-PEFT，实现端到端训练并覆盖文档中的所有token，引导LLM生成更具文档特异性的合成查询，从而进一步提升重排序性能。我们在四个公开数据集上开展了大量实验，验证了所提方法的有效性。
英文摘要：
Parameter Efficient Fine-Tuning (PEFT) methods have been extensively utilized in Large Language Models (LLMs) to improve the down-streaming tasks without the cost of fine-tuing the whole LLMs. Recent studies have shown how to effectively use PEFT for fine-tuning LLMs in ranking tasks with convincing performance; there are some limitations, including the learned prompt being fixed for different documents, overfitting to specific tasks, and low adaptation ability. In this paper, we introduce a query-dependent parameter efficient fine-tuning (Q-PEFT) approach for text reranking to leak the information of the true queries to LLMs and then make the generation of true queries from input documents much easier. Specifically, we utilize the query to extract the top-$k$ tokens from concatenated documents, serving as contextual clues. We further augment Q-PEFT by substituting the retrieval mechanism with a multi-head attention layer to achieve end-to-end training and cover all the tokens in the documents, guiding the LLMs to generate more document-specific synthetic queries, thereby further improving the reranking performance. Extensive experiments are conducted on four public datasets, demonstrating the effectiveness of our proposed approach.
CAR: Query-Guided Confidence-Aware Reranking for Retrieval-Augmented Generation
School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）；School of Information Engineering, Dalian Ocean University（大连海洋大学信息工程学院）；School of Information Engineering, Liaodong University（辽东学院信息工程学院）；Information Technology Center, Qinghai University（青海大学信息技术中心）；College of Health-Preservation and Wellness, Dalian Medical University（大连医科大学养生与健康学院）；Tencent（腾讯）
GRADA: Graph-based Reranking against Adversarial Documents Attack
University of Melbourne（墨尔本大学）；University of Edinburgh（爱丁堡大学）；University College London（伦敦大学学院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Macquarie University（麦觉里大学）
5ting at SemEval-2026 Task 8: Strong End-to-End Multi-Turn RAG via LLM-Based Reranking and Faithfulness Control
University of Information Technology, Ho Chi Minh City, Vietnam（信息技术大学，胡志明市，越南）；Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam（越南胡志明市国家大学，胡志明市，越南）
ProGRank: Probe-Gradient Reranking to Defend Dense-Retriever RAG from Corpus Poisoning
Chalmers University of Technology, Sweden（瑞典查尔姆斯理工大学）；University of Leeds, United Kingdom（英国利兹大学）；Carl von Ossietzky University of Oldenburg, Germany（德国奥尔登堡卡尔·冯·奥西特齐大学）
Beyond Top-$k$ Skill Retrieval: Diversity-Aware Skill Routing for LLM Agents
Virginia Tech（弗吉尼亚理工大学）；University of Southern California（南加州大学）；Adobe Research（Adobe研究院）；Dolby Labs（杜比实验室）；Texas A&M University（德克萨斯A&M大学）；Arizona State University（亚利桑那州立大学）
A Systematic Study of Retrieval Pipeline Design for Retrieval-Augmented Medical Question Answering
Department of Mechatronics & Industrial Engineering, Chittagong University of Engineering & Technology（吉大港工程与技术大学机电与工业工程系）；Department of Mechanical Engineering, Chittagong University of Engineering & Technology（吉大港工程与技术大学机械工程系）
PankRAG: Enhancing Graph Retrieval via Globally Aware Query Resolution and Dependency-Aware Reranking Mechanism
College of Computer Science, Beijing university of technology, Beijing, China（北京理工大学计算机科学学院）；Department of Computer Science and Engineering, Bologna University, Bologna, Italy（博洛尼亚大学计算机科学与工程系）
LegalMALR:Multi-Agent Query Understanding and LLM-Based Reranking for Chinese Statute Retrieval
Faculty of Data Science（数据科学学院）；City University of Macau（澳门城市大学）；Shenzhen Key Laboratory for High Performance Data Mining（深圳高性能数据挖掘重点实验室）；Shenzhen Institutes of Advanced Technology（深圳先进技术研究所）；Chinese Academy of Sciences（中国科学院）；Southern University of Science and Technology（南方科技大学）；Artificial Intelligence Research Institute（人工智能研究院）；Shenzhen University of Advanced Technology（深圳大学先进技术学院）
GRASP: Plan-Guided Graph Retrieval with Adaptive Fusion and Reranking on Semi-Structured Knowledge Bases
Department of Electrical Engineering and Computer Science, University of Michigan（密歇根大学电气工程与计算机科学系）；Department of Computational Medicine and Bioinformatics, University of Michigan（密歇根大学计算医学与生物信息学系）
Artificial Entanglement in the Fine-Tuning of Large Language Models
Department of Computer Science, University of Pittsburgh, Pittsburgh, PA 15260, USA（匹兹堡大学计算机科学系）；Department of Computer Science, Northwestern University, Evanston, IL 60208, USA（西北大学计算机科学系）
