In this paper, we analyze and empirically show that the learned relevance for conventional information retrieval (IR) scenarios may be inconsistent in retrieval-augmented generation (RAG) scenarios. To bridge this gap, we introduce OpenRAG, a RAG framework that is optimized end-to-end by tuning the retriever to capture in-context relevance, enabling adaptation to the diverse and evolving needs. Extensive experiments across a wide range of tasks demonstrate that OpenRAG, by tuning a retriever end-to-end, leads to a consistent improvement of 4.0% over the original retriever, consistently outperforming existing state-of-the-art retrievers by 2.1%. Additionally, our results indicate that for some tasks, an end-to-end tuned 0.2B retriever can achieve improvements that surpass those of RAG-oriented or instruction-tuned 8B large language models (LLMs), highlighting the cost-effectiveness of our approach in enhancing RAG systems.
Optimizing Multi-Hop Document Retrieval Through Intermediate Representations
School of Software, BNRist, Tsinghua University（软件学院、BNRist、清华大学）；Beijing Key Laboratory of Industrial Bigdata System and Application（北京工业大数据系统与应用重点实验室）；Gaoling School of Artificial Intelligence, Renmin University of China（甘露人工智能学院、中国人民大学）
Optimizing Retrieval-Augmented Generation of Medical Content for Spaced Repetition Learning
Poznań University of Medical Sciences（波兹南医科大学）；Adam Mickiewicz University（亚当·密茨凯维奇大学）；Wydawnictwo Naukowe PWN（PWN科学出版社）；SuperMemo World（SuperMemo世界公司）；Association for Research and Applications of Artificial Intelligence（人工智能研究与应用协会）
Optimizing Medical Question-Answering Systems: A Comparative Study of Fine-Tuned and Zero-Shot Large Language Models with RAG Framework
Department of Electrical Engineering；Computer Science University of Toledo Toledo, USA；Institute of Mathematical Sciences Claremont Graduate University Claremont, USA；Department of Bioengineering University of Toledo Toledo, USA；Department of Computer Science Bowling Green State University Bowling Green, USA
Optimizing Knowledge Integration in Retrieval-Augmented Generation with Self-Selection
University of Science and Technology of China（中国科学技术大学）；National University of Singapore（新加坡国立大学）；Institute of Dataspace, Hefei Comprehensive National Science Center（合肥综合性国家科学中心数据空间研究院）
CDS4RAG: Cyclic Dual-Sequential Hyperparameter Optimization for RAG
School of Computer Science and Engineering, UESTC, Chengdu, China（电子科技大学计算机科学与工程学院，成都，中国）；IDEAS Lab, University of Birmingham, Birmingham, UK（伯明翰大学IDEAS实验室，英国布里斯托尔）
U-NIAH: Unified RAG and LLM Evaluation for Long Context Needle-In-A-Haystack
Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（同济大学上海智能自主系统研究院）；School of Computer Science, Fudan University（复旦大学计算机科学技术学院）；College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics（南京航空航天大学人工智能学院）；College of Design and Innovation, Tongji University（同济大学设计与创新学院）
DACL-RAG: Data Augmentation Strategy with Curriculum Learning for Retrieval-Augmented Generation
School of Information Science and Technology, University of Science and Technology of China（信息科学与技术学院，中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（人工智能研究院，合肥综合性国家科学中心）
CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models
University of Science and Technology of China（中国科学技术大学）；Institute for Advanced Algorithms Research (Shanghai)（（上海）高级算法研究院）；Renmin University of China（中国人民大学）；AI Research Institute（360人工智能研究院）
First Token Probability Guided RAG for Telecom Question Answering
Shenzhen Research Institute of Big Data（深圳大数据研究院）；School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳）理工学院）；School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）
Open-RAG: Enhanced Retrieval-Augmented Reasoning with Open-Source Large Language Models
Bangladesh University of Engineering and Technology（孟加拉工程技术大学）；Cohere For AI（Cohere AI研究团队）；University of North Texas（北得克萨斯大学）；York University（约克大学）；Salesforce Research（Salesforce研究院）；Qatar Computing Research Institute (QCRI)（卡塔尔计算研究所）
Query pipeline optimization for cancer patient question answering systems
School of Computing and Information Systems, University of Melbourne（墨尔本大学计算与信息系统学院）；Health Data Science and Biostatistics, University of Texas Southwestern Medical Center（德克萨斯西南医学中心健康数据科学与生物统计学）
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；Tsinghua University（清华大学）；Zhipu AI（智谱AI）
Retro*: Optimizing LLMs for Reasoning-Intensive Document Retrieval
University of Science and Technology of China（中国科学技术大学）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Beijing University of Posts and Telecommunications（北京邮电大学）；State Key Laboratory of Cognitive Intelligence（认知智能国家重点实验室）；Hong Kong Polytechnic University（香港理工大学）；Hong Kong University of Science and Technology（香港科技大学）
PA-RAG: RAG Alignment via Multi-Perspective Preference Optimization
School of Data Science and Engineering, East China Normal University（华东师范大学数据科学与工程学院）；Chinese Academy of Sciences（中国科学院）；Baidu Inc（百度公司）；Peking University（北京大学）
Graph Your Way to Inspiration: Integrating Co-Author Graphs with Retrieval-Augmented Generation for Large Language Model Based Scientific Idea Generation
School of Computing Newcastle University（计算学院新castle大学）
