Traditional similarity-based schema matching methods fail to resolve semantic ambiguities and conflicts in complex, domain-specific mappings due to a lack of commonsense and domain-specific knowledge. In parallel, large language models (LLMs) without sufficient background knowledge tend to hallucinate, further complicating schema matching. To overcome these limitations, we introduce KG-RAG4SM, a Knowledge Graph-based Retrieval-Augmented Generation for Schema Matching. This method integrates novel retrieval mechanisms, including LLM-based, vector-based, graph traversal-based, and hybrid techniques with advanced reranking schemes to extract the most relevant subgraphs from large external KGs. Our approach achieves superior results without requiring re-training, as evidenced by improvements of 39.79% in precision and 30.56% in F1 score over LLM-based state-of-the-art (SOTA) methods (e.g., Jellyfish-8B) on the MIMIC dataset, and enhancements of 69.20% in precision and 21.99% in F1 score over pre-trained language model (PLM)-based SOTA methods (e.g., SMAT) on the Synthea dataset. Additionally, KG-RAG4SM scales effectively across KGs from commonsense KGs to domain-specific ones, while its instruction-tuning knowledge exclusion component ensures truthful performance in end-to-end schema matching tasks despite the suboptimal relevance of KG context. Our empirical analysis and case studies from real-world scenarios show that the hallucination problem in LLMs for schema matching is well mitigated by our solution.
FollowTable: A Benchmark for Instruction-Following Table Retrieval
Southeast University（东南大学）；Huawei Noah’s Ark Lab（华为诺亚实验室）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education（新一代人工智能技术及其交叉应用国家重点实验室（东南大学））；Its Interdisciplinary Applications (Southeast University), Ministry of Education（交叉应用（东南大学），教育部）
Retrieval-Augmented Code Generation for Universal Information Extraction
CAS Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所网络数据科学与技术重点实验室）；School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）；Platform and Content Group, Tencent（腾讯平台与内容事业群）
Towards Comprehensive Vietnamese Retrieval-Augmented Generation and Large Language Models
Foundation Models Lab, BKAI（BKAI基础模型实验室）；Hanoi University of Science and Technology（河内科技大学）；University of Information Technology（信息技术大学）；Vietnam National University HCMC（胡志明市越南国家大学）
Beyond the Parameters: A Technical Survey of Contextual Enrichment in Large Language Models: From In-Context Prompting to Causal Retrieval-Augmented Generation
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）
Improving Context Fidelity via Native Retrieval-Augmented Reasoning
DIRO, Université de Montréal（蒙特利尔大学计算机科学与运筹学系）；MetaGPT；Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）；McGill University（麦吉尔大学）；Yale University（耶鲁大学）；Canada CIFAR AI Chair（加拿大CIFAR人工智能教席）
Schemora: schema matching via multi-stage recommendation and metadata enrichment using off-the-shelf llms
Principal Machine Learning Engineer Informatica（Informatica）；Computer Science University of Wisconsin-Madison（威斯康星大学麦迪逊分校计算机科学系）；Director of AI/ML Development Informatica（Informatica）
Needles in the 'Sheet'stack: Augmented Analytics to get Insights from Spreadsheets
用于数据库分析的商业智能（BI）工具已经走过了漫长的道路，现在也提供了现成的见解或可视化查询探索，例如Microsoft Power BI的QuickInsights，Zenvision的SpotIQ等。在此演示中，我们专注于通过检查不同报告的定期电子表格（又名视图）来提供见解，而无需事先了解数据库或报告的架构或数据信息。这样的解决方案针对的是不熟悉数据库模式或资源的用户，以当代方式进行分析。
