Generative AI (GenAI) is expected to play a pivotal role in enabling autonomous optimization in future wireless networks. Within the ORAN architecture, Large Language Models (LLMs) can be specialized to generate xApps and rApps by leveraging specifications and API definitions from the RAN Intelligent Controller (RIC) platform. However, fine-tuning base LLMs for telecom-specific tasks remains expensive and resource-intensive. Retrieval-Augmented Generation (RAG) offers a practical alternative through in-context learning, enabling domain adaptation without full retraining. While traditional RAG systems rely on vector-based retrieval, emerging variants such as GraphRAG and Hybrid GraphRAG incorporate knowledge graphs or dual retrieval strategies to support multi-hop reasoning and improve factual grounding. Despite their promise, these methods lack systematic, metric-driven evaluations, particularly in high-stakes domains such as ORAN. In this study, we conduct a comparative evaluation of Vector RAG, GraphRAG, and Hybrid GraphRAG using ORAN specifications. We assess performance across varying question complexities using established generation metrics: faithfulness, answer relevance, context relevance, and factual correctness. Results show that both GraphRAG and Hybrid GraphRAG outperform traditional RAG. Hybrid GraphRAG improves factual correctness by 8%, while GraphRAG improves context relevance by 11%.
SafeRAG: Benchmarking Security in Retrieval-Augmented Generation of Large Language Model
School of Information, Renmin University of China（中国人民大学信息学院）；Institute for Advanced Algorithms Research（高级算法研究院）；School of Artificial Intelligence, Beihang University（北京航空航天大学人工智能学院）
Benchmarking Retrieval-Augmented Generation for Chemistry
Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校Siebel计算与数据科学学院）；National Library of Medicine, National Institutes of Health（美国国立卫生研究院国家医学图书馆）
Diagnosing and Addressing Pitfalls in KG-RAG Datasets: Toward More Reliable Benchmarking
Rensselaer Polytechnic Institute（罗切斯特理工学院）；University of Toronto（多伦多大学）；Pennsylvania State University（宾夕法尼亚州立大学）；AT&T Chief Data Office（AT&T首席数据办公室）；Griffith University（格里菲斯大学）
Benchmarking Multimodal RAG through a Chart-based Document Question-Answering Generation Framework
College of Computer Science, Chongqing University（重庆大学计算机学院）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；Kuaishou Technology（快手科技）
SciRerankBench: Benchmarking Rerankers Towards Scientific Retrieval-Augmented Generation with LLMs
Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；University of California, Los Angeles（加州大学洛杉矶分校）；Peking University（北京大学）
QHackBench: Benchmarking Large Language Models for Quantum Code Generation Using PennyLane Hackathon Challenges
New York University (NYU) Abu Dhabi（纽约大学阿布扎比分校）；Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute, New York University Abu Dhabi（纽约大学阿布扎比分校NYUAD研究所量子与拓扑系统中心）
LibRec: Benchmarking Retrieval-Augmented LLMs for Library Migration Recommendations
School of Computer and Computing Science, Hangzhou City University（杭州城市大学计算机与计算科学学院）；Polytechnic Institute, Zhejiang University（浙江大学多技术学院）；School of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学学院）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学深圳学院）；College of Computer Science and Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）；School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算机与信息科学学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study
Department of Data Science & AI, Monash University（数据科学与人工智能系，莫纳什大学）；Faculty of Law, Monash University（法学院，莫纳什大学）
Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA
MAIS, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所 MAIS）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Alibaba Group（阿里巴巴集团）
What Breaks Knowledge Graph based RAG? Benchmarking and Empirical Insights into Reasoning under Incomplete Knowledge
University of Oslo（奥斯陆大学）；Bosch Center for AI（博世人工智能中心）；University of Stuttgart（斯图加特大学）；Amazon（亚马逊公司）；University of Oxford（牛津大学）；The University of Manchester（曼彻斯特大学）；University of Southampton（南安普顿大学）
RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
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）
Agentic and Generative AI for Open-Source Intelligence and Cyber Investigations: Taxonomy, Evaluation, Challenges, and Future Directions
School of Computer Science and Mathematics, Keele University（基尔大学计算机科学与数学学院）；Cybersecurity Institute, University of Liverpool（利物浦大学网络安全研究所）；Department of Applied Computing IICL, University of Wales Trinity Saint David（威尔士特里尼达大学应用计算系）；Deakin Cyber Research and Innovation Hub, Deakin University（德金大学网络安全研究与创新中心）；Department of Information Systems and Cyber Security, The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校信息系与网络安全系）
