@inproceedings{liu-etal-2023-jarvix,
title = "{J}arvi{X}: A {LLM} No code Platform for Tabular Data Analysis and Optimization",
author = "Liu, Shang-Ching and
Wang, ShengKun and
Chang, Tsungyao and
Lin, Wenqi and
Hsiung, Chung-Wei and
Hsieh, Yi-Chen and
Cheng, Yu-Ping and
Luo, Sian-Hong and
Zhang, Jianwei",
editor = "Wang, Mingxuan and
Zitouni, Imed",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-industry.59",
doi = "10.18653/v1/2023.emnlp-industry.59",
pages = "622--630",
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.",
}
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%0 Conference Proceedings
%T JarviX: A LLM No code Platform for Tabular Data Analysis and Optimization
%A Liu, Shang-Ching
%A Wang, ShengKun
%A Chang, Tsungyao
%A Lin, Wenqi
%A Hsiung, Chung-Wei
%A Hsieh, Yi-Chen
%A Cheng, Yu-Ping
%A Luo, Sian-Hong
%A Zhang, Jianwei
%Y Wang, Mingxuan
%Y Zitouni, Imed
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F liu-etal-2023-jarvix
%X 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.
%R 10.18653/v1/2023.emnlp-industry.59
%U https://aclanthology.org/2023.emnlp-industry.59
%U https://doi.org/10.18653/v1/2023.emnlp-industry.59
%P 622-630
Markdown (Informal)
[JarviX: A LLM No code Platform for Tabular Data Analysis and Optimization](https://aclanthology.org/2023.emnlp-industry.59) (Liu et al., EMNLP 2023)
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.