In this work, we investigate the use of Large Language Models (LLMs) within a graph-based Retrieval Augmented Generation (RAG) architecture for Energy Efficiency (EE) Question Answering. First, the system automatically extracts a Knowledge Graph (KG) from guidance and regulatory documents in the energy field. Then, the generated graph is navigated and reasoned upon to provide users with accurate answers in multiple languages. We implement a human-based validation using the RAGAs framework properties, a validation dataset comprising 101 question-answer pairs, and domain experts. Results confirm the potential of this architecture and identify its strengths and weaknesses. Validation results show how the system correctly answers in about three out of four of the cases (75.2 +- 2.7%), with higher results on questions related to more general EE answers (up to 81.0 +- 4.1%), and featuring promising multilingual abilities (4.4% accuracy loss due to translation).
RAG-IGBench: Innovative Evaluation for RAG-based Interleaved Generation in Open-domain Question Answering
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China（认知智能国家重点实验室，中国科学技术大学）；Xiaohongshu Inc.（小红书公司）；Xi’an Jiaotong University（西安交通大学）
University of Macau（澳门大学）；Xiamen University（厦门大学）；Peking University（北京大学）；Hanyang University（翰阳大学）；University of Liverpool（利物浦大学）；Zhejiang University（浙江大学）
Collab-RAG: Boosting Retrieval-Augmented Generation for Complex Question Answering via White-Box and Black-Box LLM Collaboration
Emory University（埃默里大学）；University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）；Georgia Institute of Technology（佐治亚理工学院）；SUNY Albany（纽约州立大学奥尔巴尼分校）
Exploring the Role of Knowledge Graph-Based RAG in Japanese Medical Question Answering with Small-Scale LLMs
The University of Tokyo（东京大学）；NEC Laboratories America（NEC美国实验室）；Dokkyo Medical University（多摩医学大学）；Graduate School of Frontier Sciences, The University of Tokyo（东京大学前沿科学研究生院）
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）
Confident RAG: Enhancing the Performance of LLMs for Mathematics Question Answering through Multi-Embedding and Confidence Scoring
Faculty of Education, The University of Hong Kong（香港大学教育学院）；Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology（香港科学与技术大学土木与环境工程系）
NeuSym-RAG: Hybrid Neural Symbolic Retrieval with Multiview Structuring for PDF Question Answering
MoE Key Lab of Artificial Intelligence（摩尔电子关键实验室）；X-LANCE Lab, School of Computer Science, Shanghai Jiao Tong University（X-LANCE实验室，计算机科学学院，上海交通大学）；Jiangsu Key Lab of Language Computing（江苏语言计算重点实验室）；AISpeech Co., Ltd.（AISpeech公司）；Suzhou Laboratory（苏州实验室）
RAG-BioQA: A Retrieval-Augmented Generation Framework for Long-Form Biomedical Question Answering
Department of CSE Emory University Georgia, USA（计算机科学与工程系 埃默里大学 美国佐治亚州）；Department of CSE SRM University AP Andhra Pradesh, India（计算机科学与工程系 SRM大学AP 安得拉邦印度）；Department of CSE Trine University Michigan, USA（计算机科学与工程系 特林大学 密歇根州美国）
From RAG to Agentic RAG for Faithful Islamic Question Answering
Qatar Computing Research Institute, HBKU, Qatar（卡塔尔计算研究中心，HBKU，卡塔尔）；College of Humanities and Social Sciences, HBKU, Qatar（人文与社会科学学院，HBKU，卡塔尔）；Arab Center for Research and Policy Studies, Qatar（阿拉伯研究中心与政策研究所，卡塔尔）；College of Islamic Studies, HBKU, Qatar（伊斯兰研究学院，HBKU，卡塔尔）；College of Public Policy, HBKU, Qatar（公共政策学院，HBKU，卡塔尔）
HyCE-RAG: Hypergraph Chain-of-Evidence Retrieval-Augmented Generation for Explainable Multi-hop Question Answering
University of Macau（澳门大学）；Shenzhen Kaihong Digital Industry Development Co., Ltd.（深圳开鸿数字产业发展有限公司）；University of New South Wales（新南威尔士大学）；Zhejiang University（浙江大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
