# QUIS: Question-guided Insights Generation for Automated Exploratory Data Analysis

arXiv: https://arxiv.org/abs/2410.10270 · HTML: https://mlsanity.com/paper/2410.10270/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 41 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Builds on (most cited first, 8 of 8)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Text2Analysis: A Benchmark of Table Question Answering with Advanced Data Analysis and Unclear Queries](https://mlsanity.com/paper/2312.13671/) (2023)
- [Automated Data Visualization from Natural Language via Large Language Models: An Exploratory Study](https://mlsanity.com/paper/2404.17136/) (2024)
- [Foresight: Recommending Visual Insights](https://mlsanity.com/paper/1707.03877/) (2017)
- [Talk2Data: A Natural Language Interface for Exploratory Visual Analysis via Question Decomposition](https://mlsanity.com/paper/2107.14420/) (2021)
- [Beyond Interestingness: Semantic and Context-Aware Natural Language Query Recommendations for Visual Data Analysis](https://mlsanity.com/paper/2201.04868/) (2022)
- [Capture the Flag: Uncovering Data Insights with Large Language Models](https://mlsanity.com/paper/2312.13876/) (2023)
- [LINX: A Language Driven Generative System for Goal-Oriented Automated Data Exploration](https://mlsanity.com/paper/2406.05107/) (2024)

## Built on by (most cited first, 3 of 3)

- [Agentic Control Center for Data Product Optimization](https://mlsanity.com/paper/2603.10133/) (2026)
- [Behavioral Intelligence Platforms: From Event Streams to Autonomous Insight via Probabilistic Journey Graphs, Behavioral Knowledge Extraction, and Grounded Language Generation](https://mlsanity.com/paper/2604.22762/) (2026)
- [AdaLens: Interactive Storyline for Monitoring and Steering Long-Running Agentic Data Analysis](https://mlsanity.com/paper/2608.17834/) (2026)

## Abstract (arXiv)

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.