Explanova: Automatically Discover Data Insights in N \times M Table via XAI Combined LLM Workflow

TL;DR

Explanova introduces an automated data insight discovery framework for N×M tables, combining XAI and LLM workflows to efficiently explore and explain data relationships using a local small LLM.

Contribution

It proposes a novel AutoML-like workflow that systematically explores data relationships and explanations with a local small LLM, enhancing efficiency and cost-effectiveness.

Findings

Successfully discovers data insights in various tables

Reduces computational costs with local small LLM

Provides accurate and interpretable explanations

Abstract

Automation in data analysis has been a long-time pursuit. Current agentic LLM shows a promising solution towards it. Like DeepAnalyze, DataSage, and Datawise. They are all powerful agentic frameworks for automatic fine-grained analysis and are powered by LLM-based agentic tool calling ability. However, what about powered by a preset AutoML-like workflow? If we traverse all possible exploration, like Xn itself`s statistics, Xn1-Xn2 relationships, Xn to all other, and finally explain? Our Explanova is such an attempt: Cheaper due to a Local Small LLM.