Summary of Quis: Question-guided Insights Generation For Automated Exploratory Data Analysis, by Abhijit Manatkar et al.
QUIS: Question-guided Insights Generation for Automated Exploratory Data Analysis
by Abhijit Manatkar, Ashlesha Akella, Parthivi Gupta, Krishnasuri Narayanam
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Databases (cs.DB); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Discovering meaningful insights from large datasets through Exploratory Data Analysis (EDA) is a challenging task that necessitates thorough exploration and analysis. Automated Data Exploration (ADE) systems leverage Large Language Models and Reinforcement Learning for full automation, but these methods require human involvement to anticipate goals, limiting insight extraction. In contrast, fully automated systems demand significant computational resources and retraining for new datasets. This paper introduces QUIS, a fully automated EDA system operating in two stages: question generation (QUGen) driven by insight generation (ISGen). The QUGen module iteratively generates questions to refine them, enhancing 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to find important information hidden in a huge amount of data! This is called Exploratory Data Analysis (EDA). Right now, machines can help with this task using large language models and learning. But these machines need humans to tell them what to look for, which limits how much they can find. The new QUIS system tries to solve this problem by creating a machine that can explore data all on its own! This machine works in two parts: it first generates questions about the data, then uses those questions to find answers and discover important insights. |
Keywords
» Artificial intelligence » Reinforcement learning
