Generating Statistical Charts with Validation-Driven LLM Workflows

TL;DR

This paper introduces a validation-driven LLM workflow for generating diverse, high-quality statistical charts from tabular data, addressing visualization failures and providing aligned artifacts.

Contribution

It presents a structured, multi-step process for chart generation that improves reliability and diagnostic analysis of multimodal LLMs in visualization tasks.

Findings

Generated 1,500 charts from 74 datasets across 24 chart types.

Evaluated 16 multimodal LLMs on chart-question pairs, revealing strengths and challenges.

Chart-syntax questions are nearly saturated; reasoning tasks remain difficult.

Abstract

Generating diverse, readable statistical charts from tabular data remains challenging for LLMs, as many failures become apparent after rendering and are not detectable from data or code alone. Existing chart datasets also rarely provide fully aligned artifacts, such as executable code, dataset context, and question-answer pairs. We present a structured LLM-based workflow that decomposes chart generation into dataset screening, plot proposal, code synthesis, rendering, validation-driven refinement, description generation, and question-answer generation. By incorporating rendered-output validation, the workflow addresses visualization-specific failure modes such as readability and semantic mismatch. It treats chart generation as an inspectable process rather than a one-shot prompt-to-code task, retaining each chart with its code, dataset context, description, and question-answer pairs. Applied to UCI datasets, the workflow produces 1,500 charts from 74 datasets, spanning 24 chart families and paired with 30,003 question-answer pairs. We evaluate 16 multimodal LLMs (MLLMs) on these chart-question pairs. The results show that chart-syntax questions are nearly saturated, while value extraction, comparison, and reasoning remain more challenging, illustrating the workflow's utility for diagnostic studies of chart-grounded multimodal reasoning.