Retrieval-Augmented Generation (RAG) has become ubiquitous when deploying Large Language Models (LLMs), as it can address typical limitations such as generating hallucinated or outdated information. However, when building real-world RAG applications, practical issues arise. First, the retrieved information is generally domain-specific. Since it is computationally expensive to fine-tune LLMs, it is more feasible to fine-tune the retriever to improve the quality of the data included in the LLM input. Second, as more applications are deployed in the same real-world system, one cannot afford to deploy separate retrievers. Moreover, these RAG applications normally retrieve different kinds of data. Our solution is to instruction fine-tune a small retriever encoder on a variety of domain-specific tasks to allow us to deploy one encoder that can serve many use cases, thereby achieving low-cost, scalability, and speed. We show how this encoder generalizes to out-of-domain settings as well as to an unseen retrieval task on real-world enterprise use cases.
Quantifying Retriever-Generator Alignment in RAG with Local Explanations
Department of Computer and Systems Sciences, Stockholm University（斯德哥尔摩大学计算机与系统科学系）；BIFOLD, Technische Universität Berlin（柏林技术大学BIFOLD）；Department of Informatics, Athens University of Economics and Business（雅典经济与商业大学信息系）；Archimedes, Athena Research Centre（雅典研究中心Archimedes）
Neurosymbolic Retrievers for Retrieval-augmented Generation
Dept. of CSEE University of Maryland Baltimore County, Maryland, USA（电子工程系大学马里兰大学巴尔的摩县）；Dept. of CSEE University of Maryland Baltimore County, MD, USA（电子工程系大学马里兰大学巴尔的摩县）
Understanding the Behaviors of Environment-aware Information Retrieval
Fudan University（复旦大学）；Alibaba DAMO Academy（阿里巴巴达摩院）；Chinese University of Hong Kong（香港中文大学）；Stanford University（斯坦福大学）；Shanghai University of Finance and Economics（上海财经大学）
IRLab, Informatics Institute, UvA, Amsterdam, the Netherlands（IR实验室、信息研究所、乌得勒支大学、阿姆斯特丹、荷兰）；AI Lab, Pegasystems, Amsterdam, the Netherlands（AI实验室、佩加系统的阿姆斯特丹分部）；LIACS, Leiden University, Leiden, the Netherlands（LIACS、莱顿大学、莱顿、荷兰）
LevelRAG: Enhancing Retrieval-Augmented Generation with Multi-hop Logic Planning over Rewriting Augmented Searchers
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Chinese Academy of Sciences（中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
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）
C-3PO: Compact Plug-and-Play Proxy Optimization to Achieve Human-like Retrieval-Augmented Generation
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学人工智能学院）；Tongyi Lab（通义实验室）；Soochow University（苏州大学）；University of Oxford（牛津大学）；Tsinghua University（清华大学）
R^2AG: Incorporating Retrieval Information into Retrieval Augmented Generation
School of Computer Science, South China Normal University（华南师范大学计算机学院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）
Beyond Topical Similarity: Contrastive Evidence Retrieval with Interpretable Attention Alignment in RAG
University of Chile（智利大学）；São Paulo State University（圣保罗州立大学）；Saarland University（萨尔兰州立大学）；University of Munich（慕尼黑大学）；Portland State University（波特兰州立大学）；Idiap Research Institute（Idiap研究机构）
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）
