JarviX: A LLM No code Platform for Tabular Data Analysis and
  Optimization
Shang-Ching Liu ShengKun Wang Wenqi Lin Chung-Wei Hsiung Yi-Chen Hsieh Yu-Ping Cheng Sian-Hong Luo Tsungyao Chang Jianwei Zhang
In this study, we introduce JarviX, a sophisticated data analytics framework.
JarviX is designed to employ Large Language Models (LLMs) to facilitate an
automated guide and execute high-precision data analyzes on tabular datasets.
This framework emphasizes the significance of varying column types,
capitalizing on state-of-the-art LLMs to generate concise data insight
summaries, propose relevant analysis inquiries, visualize data effectively, and
provide comprehensive explanations for results drawn from an extensive data
analysis pipeline. Moreover, JarviX incorporates an automated machine learning
(AutoML) pipeline for predictive modeling. This integration forms a
comprehensive and automated optimization cycle, which proves particularly
advantageous for optimizing machine configuration. The efficacy and
adaptability of JarviX are substantiated through a series of practical use case
studies.