Pith. sign in

REVIEW 1 cited by

Data Formulator: AI-powered Concept-driven Visualization Authoring

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 2309.10094 v2 pith:JLP7ULNW submitted 2023-09-18 cs.HC cs.AI

classification cs.HCcs.AI
keywords dataformulatorvisualizationvisualizationsauthoringauthorsagentconcepts
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

With most modern visualization tools, authors need to transform their data into tidy formats to create visualizations they want. Because this requires experience with programming or separate data processing tools, data transformation remains a barrier in visualization authoring. To address this challenge, we present a new visualization paradigm, concept binding, that separates high-level visualization intents and low-level data transformation steps, leveraging an AI agent. We realize this paradigm in Data Formulator, an interactive visualization authoring tool. With Data Formulator, authors first define data concepts they plan to visualize using natural languages or examples, and then bind them to visual channels. Data Formulator then dispatches its AI-agent to automatically transform the input data to surface these concepts and generate desired visualizations. When presenting the results (transformed table and output visualizations) from the AI agent, Data Formulator provides feedback to help authors inspect and understand them. A user study with 10 participants shows that participants could learn and use Data Formulator to create visualizations that involve challenging data transformations, and presents interesting future research directions.

Discussion (0). Continue with ORCID 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. MDSF: Context-Aware Multi-Dimensional Data Storytelling Framework based on Large language Model

    cs.CL 2025-01 reject novelty 4.0 of 10

    MDSF is an LLM-based framework for automated data insight ranking and storytelling that, by its own reported results, does not outperform GPT-4 on ranking and most narrative metrics.

Pith tools