上交、清华、微软、上海AI Lab等联合发布数据分析智能体综述，LLM化身数据分析师，让数据自己「说话」

Core Insights - The article discusses the evolution of data analysis through the integration of large language models (LLMs) and agents, moving from traditional rule-based systems to intelligent systems that understand data semantics [2][4][11] - It emphasizes the need for a General Data Analyst Agent paradigm that can handle various data types and tasks, enhancing the capabilities of data analysis [4][11] Group 1: Evolution of Data Analysis - Traditional data analysis methods rely on manual processes such as SQL coding and Python scripting, which are high in coupling and low in scalability [2] - The emergence of LLMs and agents allows for a shift from rule execution to semantic understanding, enabling machines to interpret the underlying logic and relationships in data [2][10] - The research identifies four core evolution directions for LLM/Agent technology in data analysis, aiming to transform data analysis from a rule-based system to an intelligent agent system [7][11] Group 2: Key Technical Directions - The article outlines five major directions in data analysis technology: semantic understanding, autonomous pipelines, automated workflows, tool collaboration, and open-world orientation [4][10] - It highlights the transition from closed tools to collaborative models that can interact with external APIs and knowledge bases for complex tasks [10] - The focus is on enabling dynamic generation of workflows, allowing agents to automatically construct analysis processes, enhancing efficiency and flexibility [10] Group 3: Data Types and Analysis Techniques - The article categorizes data into structured, semi-structured, unstructured, and heterogeneous data, detailing specific tasks and technologies for each type [9][12] - For structured data, it discusses advancements in relational data analysis and graph data analysis, emphasizing the shift from code-level to semantic-level understanding [9][12] - Semi-structured data analysis includes tasks like markup language understanding and semi-structured table comprehension, transitioning from template-driven approaches to LLM-based methods [12] - Unstructured data analysis covers document understanding, chart interpretation, and video/3D model analysis, integrating various technologies for comprehensive understanding [12] Group 4: Future Challenges - The article identifies future challenges in scalability, evaluation systems, and practical implementation of general data analysis agents [4][11] - It stresses the importance of robustness and adaptability to open-domain scenarios as critical factors for the success of these intelligent agents [11]