ChartM$^3$: A Multi-Stage Code-Driven Pipeline for Constructing Multi-Dimensional and Multi-Step Visual Reasoning Data in Chart Comprehension

TL;DR

This paper introduces ChartM$^3$, a comprehensive dataset generated through a multi-stage code-driven pipeline, to enhance visual reasoning and understanding in chart comprehension tasks for multimodal large language models.

Contribution

The study presents a novel automated pipeline for creating complex chart datasets, significantly improving data diversity and reasoning capabilities in chart comprehension models.

Findings

Dataset contains 38K charts and 142K Q&A pairs.

Models trained on ChartM$^3$ outperform baselines in reasoning tasks.

Enhanced cross-domain generalization in chart understanding.

Abstract

Complex chart understanding tasks demand advanced visual recognition and reasoning capabilities from multimodal large language models (MLLMs). However, current research provides limited coverage of complex chart scenarios and computation-intensive reasoning tasks prevalent in real-world applications. This study proposes an automated multi-stage code-driven pipeline for systematically generating visual reasoning datasets to address these limitations. The pipeline integrates retrieval-augmented generation (RAG) to retrieve professional chart templates and employs chain-of-thought (CoT) strategies to generate reasoning codes that simulate real data distributions, thereby driving chart rendering and question-related statistical computations. Through model-based evaluation, the pipeline enhances chart diversity and data quality. Using this framework, we construct ChartM$^3$, a multi-dimensional and multi-step dataset containing 38K charts and 142K Q&A pairs for training, along with 2,871 high-quality evaluation samples for enabling practical performance assessment. Supervised fine-tuning (SFT) and reinforcement learning (RL) experiments demonstrate that our dataset significantly improves reasoning capabilities and cross-domain generalization performance, enabling smaller models to achieve performance comparable to larger-scale models in complex chart comprehension.