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.
@article{arxiv.2312.02213,
title = {JarviX: A LLM No code Platform for Tabular Data Analysis and Optimization},
author = {Shang-Ching Liu and ShengKun Wang and Wenqi Lin and Chung-Wei Hsiung and Yi-Chen Hsieh and Yu-Ping Cheng and Sian-Hong Luo and Tsungyao Chang and Jianwei Zhang},
journal= {arXiv preprint arXiv:2312.02213},
year = {2023}
}