JarviX: A LLM No code Platform for Tabular Data Analysis and Optimization
Not on the map. Why?
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
- 2022Scaling Instruction-Finetuned Language Modelscited by 3,024
- 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
- 2016End-to-End Relation Extraction using LSTMs on Sequences and Tree Structurescited by 161
- 2023ChatDB: Augmenting LLMs with Databases as Their Symbolic Memorycited by 140
- 2021Generate & Rank: A Multi-task Framework for Math Word Problemscited by 82
- 2023XNLI: Explaining and Diagnosing NLI-based Visual Data Analysiscited by 19
Built on by
Citing papers per year: 2024: 6; 2025: 4.
10 papers.
- 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 159
- 2025AutoReproduce: Automatic AI Experiment Reproduction with Paper Lineagecited by 32
- 2024LLMs Are Biased Towards Output Formats! Systematically Evaluating and Mitigating Output Format Bias of LLMscited by 25
- 2024A Survey on Large Language Model-based Agents for Statistics and Data Sciencecited by 22
- 2024LightVA: Lightweight Visual Analytics with LLM Agent-Based Task Planning and Executioncited by 20
- 2024Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Surveycited by 19
- 2025Tab-MIA: A Benchmark Dataset for Membership Inference Attacks on Tabular Data in LLMscited by 3
- 2025Optimizing Code Runtime Performance through Context-Aware Retrieval-Augmented Generationcited by 1
- 2024SLEGO: A Collaborative Data Analytics System with LLM Recommender for Diverse Userscited by 0
- 2025Linking Heterogeneous Data with Coordinated Agent Flows for Social Media Analysiscited by 0
- 2025Linking Heterogeneous Data with Coordinated Agent Flows for Social Media Analysis30 Oct 2025
- 2025Tab-MIA: A Benchmark Dataset for Membership Inference Attacks on Tabular Data in LLMs23 Jul 2025
- 2025AutoReproduce: Automatic AI Experiment Reproduction with Paper Lineage27 May 2025
- 2025Optimizing Code Runtime Performance through Context-Aware Retrieval-Augmented Generation28 Jan 2025
- 2024A Survey on Large Language Model-based Agents for Statistics and Data Science18 Dec 2024
- 2024Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey11 Nov 2024
- 2024LightVA: Lightweight Visual Analytics with LLM Agent-Based Task Planning and Execution8 Nov 2024
- 2024LLMs Are Biased Towards Output Formats! Systematically Evaluating and Mitigating Output Format Bias of LLMs16 Aug 2024
- 2024SLEGO: A Collaborative Data Analytics System with LLM Recommender for Diverse Users17 Jun 2024
- 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey27 Feb 2024