JarviX: A LLM No code Platform for Tabular Data Analysis and Optimization

Shang-Ching Liu, ShengKun Wang, Tsungyao Chang, Wenqi Lin, Chung-Wei Hsiung, Yi-Chen Hsieh, Yu-Ping Cheng, Sian-Hong Luo, Jianwei Zhang


Abstract
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.
Anthology ID:
2023.emnlp-industry.59
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
Month:
December
Year:
2023
Address:
Singapore
Editors:
Mingxuan Wang, Imed Zitouni
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
622–630
Language:
URL:
https://aclanthology.org/2023.emnlp-industry.59
DOI:
10.18653/v1/2023.emnlp-industry.59
Bibkey:
Cite (ACL):
Shang-Ching Liu, ShengKun Wang, Tsungyao Chang, Wenqi Lin, Chung-Wei Hsiung, Yi-Chen Hsieh, Yu-Ping Cheng, Sian-Hong Luo, and Jianwei Zhang. 2023. JarviX: A LLM No code Platform for Tabular Data Analysis and Optimization. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 622–630, Singapore. Association for Computational Linguistics.
Cite (Informal):
JarviX: A LLM No code Platform for Tabular Data Analysis and Optimization (Liu et al., EMNLP 2023)
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