用大模型+人工指导，自动分析数据集风险，避免幻觉又省人力。
Towards automated data analysis: A guided framework for LLM-based risk estimation
- 大模型分析数据库结构与语义，自动生成聚类方案和代码。
- 人工监督确保分析过程准确，结果符合任务目标。
- 适合需要自动化数据风险评估的科研或企业场景。
大型语言模型（LLMs）正越来越多地融入关键决策流程，推动对强大且自动化的数据分析需求。当前的数据集风险分析主要依赖耗时复杂的手动审计，而完全基于人工智能的自动化分析则面临幻觉和对齐问题。为此，本文提出一种结合生成式AI与人工指导的框架，用于数据集风险估计，旨在为未来自动化风险分析奠定基础。该方法利用大模型识别数据库模式中的语义与结构特征，进而提出聚类技术、生成实现代码并解释结果。人类监督者引导模型聚焦分析目标，保障过程完整性和任务一致性。通过一个概念验证，展示了该框架在风险评估任务中生成有意义结果的可行性。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are increasingly integrated into critical decision-making pipelines, a trend that raises the demand for robust and automated data analysis. Current approaches to dataset risk analysis are limited to manual auditing methods which involve time-consuming and complex tasks, whereas fully automated analysis based on Artificial Intelligence (AI) suffers from hallucinations and issues stemming from AI alignment. To this end, this work proposes a framework for dataset risk estimation that integrates Generative AI under human guidance and supervision, aiming to set the foundations for a future automated risk analysis paradigm. Our approach utilizes LLMs to identify semantic and structural properties in database schemata, subsequently propose clustering techniques, generate the code for them and finally interpret the produced results. The human supervisor guides the model on the desired analysis and ensures process integrity and alignment with the task's objectives. A proof of concept is presented to demonstrate the feasibility of the framework's utility in producing meaningful results in risk assessment tasks.
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