mlsanityData to 14 Oct 2026

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

arXiv 2312.02213 ↗First posted 3 Dec 2023cs.LG cs.AI cs.DBcited by 10

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Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 6 of its 18 references that are in the map. First posted on arXiv 3 Dec 2023.

Builds on

6 of 18 references.

References · 6

  1. 2022Scaling Instruction-Finetuned Language Modelscited by 3,024
  2. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
  3. 2016End-to-End Relation Extraction using LSTMs on Sequences and Tree Structurescited by 161
  4. 2023ChatDB: Augmenting LLMs with Databases as Their Symbolic Memorycited by 140
  5. 2021Generate & Rank: A Multi-task Framework for Math Word Problemscited by 82
  6. 2023XNLI: Explaining and Diagnosing NLI-based Visual Data Analysiscited by 19

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Citing papers per year: 2024: 6; 2025: 4.

10 papers.