尽管NL2SQL领域在将自然语言指令翻译为用于数据查询和处理的可执行SQL脚本方面已取得显著进展，但在更广泛的数据科学流水线中实现完全自动化——涵盖数据查询、分析、可视化和报告——仍然是一项复杂挑战。本研究介绍了SageCopilot，这是一个先进的工业级系统，通过集成Large Language Models（LLMs）、Autonomous Agents（AutoAgents）和Language User Interfaces（LUIs）来自动化数据科学流水线。具体而言，SageCopilot采用两阶段设计：在线组件通过In-Context Learning（ICL）将用户输入细化为可执行脚本，并运行脚本以进行结果报告与可视化；离线组件则准备在线阶段ICL所需的演示示例。一系列主流策略，如Chain-of-Thought和prompt-tuning，已被用于增强SageCopilot以提升性能。通过在真实世界数据集上进行严格测试并与基于提示的解决方案进行对比分析，SageCopilot已被实证验证能够在脚本生成或执行以及提供带可视化结果方面取得优越的端到端性能。我们深入的消融研究揭示了SageCopilot所采用的各种组件和策略对数据科学端到端正确性的各自贡献。
英文摘要：
While the field of NL2SQL has made significant advancements in translating natural language instructions into executable SQL scripts for data querying and processing, achieving full automation within the broader data science pipeline - encompassing data querying, analysis, visualization, and reporting - remains a complex challenge. This study introduces SageCopilot, an advanced, industry-grade system system that automates the data science pipeline by integrating Large Language Models (LLMs), Autonomous Agents (AutoAgents), and Language User Interfaces (LUIs). Specifically, SageCopilot incorporates a two-phase design: an online component refining users' inputs into executable scripts through In-Context Learning (ICL) and running the scripts for results reporting & visualization, and an offline preparing demonstrations requested by ICL in the online phase. A list of trending strategies such as Chain-of-Thought and prompt-tuning have been used to augment SageCopilot for enhanced performance. Through rigorous testing and comparative analysis against prompt-based solutions, SageCopilot has been empirically validated to achieve superior end-to-end performance in generating or executing scripts and offering results with visualization, backed by real-world datasets. Our in-depth ablation studies highlight the individual contributions of various components and strategies used by SageCopilot to the end-to-end correctness for data sciences.
An Evaluation of the Pedagogical Soundness and Usability of AI-Generated Lesson Plans Across Different Models and Prompt Frameworks in High-School Physics
Uncertainty in Automated Ontology Matching: Lessons Learned from an Empirical Experimentation
University of Tunis El Manar（突尼斯迦太基大学）；University of Technology Sydney（悉尼科技大学）；Tallinn University of Technology（塔林理工大学）；University of Southern Denmark（南丹麦大学）
ELMES: An Automated Framework for Evaluating Large Language Models in Educational Scenarios
Shanghai Innavation Institute（上海创新研究院）；Shanghai Institute of AI for Education（上海人工智能教育研究院）；Department of Educational Information Technology（教育信息技术系）；School of Computer Science and Technology（计算机科学与技术学院）
SimpleEvol: An Agent-Loop Framework for LLM-Driven Automated Heuristic Design with Minimal Human Priors
Singapore Management University（新加坡管理大学）；Nanyang Technological University（南洋理工大学）；TikTok Singapore（新加坡TikTok）；Nanjing University of Information Science and Technology（南京信息工程大学）；National University of Singapore（新加坡国立大学）
Solving with GeoGebra Discovery an Austrian Mathematics Olympiad problem: Lessons Learned
Universidad Rey Juan Carlos（胡安·卡洛斯国王大学）；The Private University College of Education of the Diocese of Linz（林茨教区私立教育学院）；Universidad Antonio de Nebrija（安东尼奥·德·内布里哈大学）
Automated Quality Assessment of Geospatial Vector Data: A GeoAI Approach using Spatial Representation Learning
Department of Geography, National University of Singapore, Singapore（新加坡国立大学地理系）；University of Southern California（南加州大学）；Department of Architecture, National University of Singapore, Singapore（新加坡国立大学建筑系）；Department of Real Estate, National University of Singapore, Singapore（新加坡国立大学房地产系）
Advancing Italian Biomedical Information Extraction with Transformers-based Models: Methodological Insights and Multicenter Practical Application
IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli（IRCCS 圣乔瓦尼迪奥法特贝内弗拉特利研究所）；University of Pavia（帕维亚大学）；IRCCS Istituto Auxologico Italiano（IRCCS 意大利奥索洛吉科研究所）；Università degli Studi di Milano（米兰大学）；IRCCS Istituti Clinici Scientifici Maugeri（IRCCS 莫盖里临床科学研究所）；IRCCS Mondino Foundation（IRCCS 蒙迪诺基金会）
