Yi-Chen Hsieh


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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
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track

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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VocabAnalyzer: A Referred Word List Analyzing Tool with Keyword, Concordancing and N-gram Functions
Siaw-Fong Chung | F.Y. August Chao | Yi-Chen Hsieh
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 2