Retrieval-augmented generation (RAG) is increasingly recognized as an effective approach to mitigating the hallucination of large language models (LLMs) through the integration of external knowledge. While numerous efforts, most studies focus on a single type of external knowledge source. However, in real-world applications, most situations involve diverse knowledge from various sources, yet this area has been less explored. The main dilemma is the lack of a suitable dataset containing multiple knowledge sources and pre-exploration of the associated issues. To address these challenges, we standardize a benchmark dataset that combines structured and unstructured knowledge across diverse and complementary domains. Based on this dataset, we further develop a plug-and-play RAG framework, \textbf{PruningRAG}, whose main characteristic is the use of multi-granularity pruning strategies to optimize the integration of relevant information while minimizing misleading context. It consistently improves performance across various existing RAG variants, demonstrating its robustness and broad applicability. Building upon the standardized dataset and PruningRAG, we also report a series of experimental results, as well as insightful findings. Our dataset and code are publicly available\footnote{https://github.com/USTCAGI/PruningRAG}, with the aim of advancing future research in the RAG community.
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）
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））
A Large Language Model-based Framework for Semi-Structured Tender Document Retrieval-Augmented Generation
School of Information Management Sun Yat-sen University Guangzhou, China；School of Information Management Sun Yat-sen University Guangzhou, China corresponding author
IRSC: A Zero-shot Evaluation Benchmark for Information Retrieval through Semantic Comprehension in Retrieval-Augmented Generation Scenarios
PengCheng Laboratory（鹏城实验室）；Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Southern University of Science and Technology（南方科技大学）
CausalRAG: Integrating Causal Graphs into Retrieval-Augmented Generation
Department of Computer and Data Sciences, Case Western Reserve University（计算机与数据科学系，凯斯西储大学）；Department of Design and Innovation, Case Western Reserve University（设计与创新系，凯斯西储大学）
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）
Benchmarking Foundation Models with Retrieval-Augmented Generation in Olympic-Level Physics Problem Solving
AAII, University of Technology Sydney, New South Wales, Australia（AAII，悉尼大学，新南威尔士州，澳大利亚）；Eindhoven University of Technology, Eindhoven, The Netherlands（埃因霍温理工大学，埃因霍温，荷兰）；University of Liverpool, Liverpool, United Kingdom（利物浦大学，利物浦，英国）；University of New South Wales, New South Wales, Australia（新南威尔士大学，新南威尔士州，澳大利亚）
Enhancing Knowledge Density via Hierarchical Context Pruning for Retrieval-Augmented Repository-Level Code Completion
MIIT Key Laboratory of Pattern Analysis and Machine Intelligence（信息科技模式分析与机器智能国家重点实验室）；College of Computer Science and Technology（计算机科学与技术学院）；Huaneng Information Technology Co Ltd（华能信息技术有限公司）；Hunan Vanguard Group Corporation Co Ltd（湖南 Vanguard集团有限公司）；Tsinghua University（清华大学）；Zhejiang University（浙江大学）；PowerChina Huadong Engineering Co Ltd（国家电网华东工程有限公司）
Enhancing Retrieval-Augmented LMs with a Two-stage Consistency Learning Compressor
College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）；Artificial Intelligence Laboratory, China Mobile Communication Group Tianjin Co., Ltd.（中国移动通信集团天津有限公司人工智能实验室）；University College London（伦敦大学学院）
