无需人工干预，自动提问并生成数据洞察。
QUIS: Question-guided Insights Generation for Automated Exploratory Data Analysis
- 通过迭代提问优化覆盖范围，自动构建分析问题。
- 无需预训练即可生成多条相关洞察，适配新数据集。
- 全自动化流程，避免人力预设目标与重复训练。
从大型数据集中发现有意义的洞察，即探索性数据分析（EDA），是一项需要深入探索和分析的挑战性任务。自动化数据探索（ADE）系统采用以目标为导向的方法，结合大语言模型与强化学习实现完全自动化。然而，这些方法需人类预先设定目标，限制了洞察挖掘；而完全自动化系统则需大量计算资源，并在新数据集上重新训练。我们提出QUIS，一种两阶段全自动EDA系统：由问题生成（QUGen）驱动的洞察生成（ISGen）。QUGen模块通过迭代方式生成问题，不断优化问题表述以提升覆盖度，无需人工干预或人工标注示例。ISGen模块针对每个问题分析数据，生成多条相关洞察，无需预先训练，使QUIS可快速适应新数据集。
原文摘要 · Abstract (English)
Discovering meaningful insights from a large dataset, known as Exploratory Data Analysis (EDA), is a challenging task that requires thorough exploration and analysis of the data. Automated Data Exploration (ADE) systems use goal-oriented methods with Large Language Models and Reinforcement Learning towards full automation. However, these methods require human involvement to anticipate goals that may limit insight extraction, while fully automated systems demand significant computational resources and retraining for new datasets. We introduce QUIS, a fully automated EDA system that operates in two stages: insight generation (ISGen) driven by question generation (QUGen). The QUGen module generates questions in iterations, refining them from previous iterations to enhance coverage without human intervention or manually curated examples. The ISGen module analyzes data to produce multiple relevant insights in response to each question, requiring no prior training and enabling QUIS to adapt to new datasets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。