Pith. sign in

REVIEW 1 cited by

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

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2312.02213 v1 pith:RL5VXUIJ submitted 2023-12-03 cs.LG cs.AIcs.DBstat.AP

classification cs.LGcs.AIcs.DBstat.AP
keywords datajarvixanalysisautomatedcomprehensiveframeworkllmsmachine
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original 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.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery

    cs.CR 2025-07 reject novelty 3.0 of 10

    Scout applies off-the-shelf LLMs and vision models to triage digital evidence, but only anecdotal examples are shown and accuracy is withheld.

Pith tools