评测也很酷，Data Agent 自动化评测的三层框架与实战

Core Viewpoint - The article emphasizes the importance of effective evaluation methods for large model applications in the big data field, highlighting the challenges and innovations in automated evaluation techniques for AI agents [2][5]. Group 1: Evaluation Challenges - Traditional software testing methods are insufficient for evaluating large model applications due to increased complexity and the need for more relevant metrics [5][10]. - Common evaluation dimensions include factual accuracy, usefulness, harmfulness, performance, robustness, and efficiency [8][9]. - There is a disconnect between static evaluations and real-world performance, leading to discrepancies in user satisfaction [10]. Group 2: Evaluation Methods - Current evaluation methods include manual assessment, automated evaluation using objective questions, similarity comparisons, and human-machine collaborative evaluations [9]. - A three-layer evaluation framework is proposed, focusing on technical selection, iterative development, and end-to-end business effectiveness [18][20]. Group 3: Data Agent Evaluation - The evaluation of Data Agents requires addressing domain-specific challenges, such as the accuracy of SQL generation and the complexity of data sources [14][15]. - A semantic equivalence-based evaluation method is introduced to improve the accuracy of SQL assessments, addressing limitations of traditional binary evaluation methods [29][30]. - The evaluation framework for deep research products includes metrics for accuracy, completeness, readability, and stability [33][34]. Group 4: Automation in Evaluation - The use of agents to evaluate agents is explored, leveraging self-reflection and multi-agent collaboration to enhance evaluation accuracy [37][38]. - The platform for data evaluation integrates various functionalities, including dataset management, automated and manual assessments, and continuous updates based on real-world usage [45][46]. Group 5: Future Directions - Future efforts will focus on refining evaluation dimensions, improving consistency between offline and online assessments, and implementing evaluation-driven development practices [48][49].