Cover Image

ChartCoder: Advancing Multimodal Large Language Model for Chart-to-Code Generation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhao, Xuanle, Luo, Xianzhen, Shi, Qi, Chen, Chi, Wang, Shuo, Liu, Zhiyuan, Sun, Maosong
Format:	Preprint
Published:	2025
Subjects:	Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Chart-R1: Chain-of-Thought Supervision and Reinforcement for Advanced Chart Reasoner
by: Chen, Lei, et al.
Published: (2025)

Breaking the SFT Plateau: Multimodal Structured Reinforcement Learning for Chart-to-Code Generation
by: Chen, Lei, et al.
Published: (2025)

MM-ReCoder: Advancing Chart-to-Code Generation with Reinforcement Learning and Self-Correction
by: Tang, Zitian, et al.
Published: (2026)

ChartEdit: How Far Are MLLMs From Automating Chart Analysis? Evaluating MLLMs' Capability via Chart Editing
by: Zhao, Xuanle, et al.
Published: (2025)

SynChart: Synthesizing Charts from Language Models
by: Liu, Mengchen, et al.
Published: (2024)

ChartInsights: Evaluating Multimodal Large Language Models for Low-Level Chart Question Answering
by: Wu, Yifan, et al.
Published: (2024)

MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning
by: Liu, Fuxiao, et al.
Published: (2023)

AutoReproduce: Automatic AI Experiment Reproduction with Paper Lineage
by: Zhao, Xuanle, et al.
Published: (2025)

From Charts to Code: A Hierarchical Benchmark for Multimodal Models
by: Tang, Jiahao, et al.
Published: (2025)

ChartGen: Scaling Chart Understanding Via Code-Guided Synthetic Chart Generation
by: Kondic, Jovana, et al.
Published: (2025)

ChartCards: A Chart-Metadata Generation Framework for Multi-Task Chart Understanding
by: Wu, Yifan, et al.
Published: (2025)

ChartEditBench: Evaluating Grounded Multi-Turn Chart Editing in Multimodal Language Models
by: Kapadnis, Manav Nitin, et al.
Published: (2026)

Advancing Multimodal Large Language Models in Chart Question Answering with Visualization-Referenced Instruction Tuning
by: Zeng, Xingchen, et al.
Published: (2024)

InfoChartQA: A Benchmark for Multimodal Question Answering on Infographic Charts
by: Xie, Tianchi, et al.
Published: (2025)

ChartAnchor: Chart Grounding with Structural-Semantic Fidelity
by: Li, Xinhang, et al.
Published: (2025)

OmniDiagram: Advancing Unified Diagram Code Generation via Visual Interrogation Reward
by: Yang, Haoyue, et al.
Published: (2026)

ChartM$^3$: Benchmarking Chart Editing with Multimodal Instructions
by: Yang, Donglu, et al.
Published: (2025)

Chart-CoCa: Self-Improving Chart Understanding of Vision LMs via Code-Driven Synthesis and Candidate-Conditioned Answering
by: Jiang, Gongyao, et al.
Published: (2025)

WizardCoder: Empowering Code Large Language Models with Evol-Instruct
by: Luo, Ziyang, et al.
Published: (2023)

ChartDiff: A Large-Scale Benchmark for Comprehending Pairs of Charts
by: Ye, Rongtian
Published: (2026)

On Pre-training of Multimodal Language Models Customized for Chart Understanding
by: Fan, Wan-Cyuan, et al.
Published: (2024)

ChartPoint: Guiding MLLMs with Grounding Reflection for Chart Reasoning
by: Xu, Zhengzhuo, et al.
Published: (2025)

Unmasking Deceptive Visuals: Benchmarking Multimodal Large Language Models on Misleading Chart Question Answering
by: Chen, Zixin, et al.
Published: (2025)

Advancing Chart Question Answering with Robust Chart Component Recognition
by: Zheng, Hanwen, et al.
Published: (2024)

ChartKG: A Knowledge-Graph-Based Representation for Chart Images
by: Zhou, Zhiguang, et al.
Published: (2024)

Assessing Y-Axis Influence: Bias in Multimodal Language Models on Chart-to-Table Translation
by: Song, Seok Hwan, et al.
Published: (2026)

BioCoder: A Benchmark for Bioinformatics Code Generation with Large Language Models
by: Tang, Xiangru, et al.
Published: (2023)

AskChart: Universal Chart Understanding through Textual Enhancement
by: Yang, Xudong, et al.
Published: (2024)

Aligned Multi-View Scripts for Universal Chart-to-Code Generation
by: Zhang, Zhihan, et al.
Published: (2026)

Improved Iterative Refinement for Chart-to-Code Generation via Structured Instruction
by: Xu, Chengzhi, et al.
Published: (2025)

Chart-RL: Policy Optimization Reinforcement Learning for Enhanced Visual Reasoning in Chart Question Answering with Vision Language Models
by: Bai, Yunfei, et al.
Published: (2026)

Text2Chart31: Instruction Tuning for Chart Generation with Automatic Feedback
by: Zadeh, Fatemeh Pesaran, et al.
Published: (2024)

Chart-HQA: A Benchmark for Hypothetical Question Answering in Charts
by: Chen, Xiangnan, et al.
Published: (2025)

Exploring Diffusion Models for Generative Forecasting of Financial Charts
by: Lee, Taegyeong, et al.
Published: (2025)

ChartHal: A Fine-grained Framework Evaluating Hallucination of Large Vision Language Models in Chart Understanding
by: Wang, Xingqi, et al.
Published: (2025)

Model Composition for Multimodal Large Language Models
by: Chen, Chi, et al.
Published: (2024)

PerfCoder: Large Language Models for Interpretable Code Performance Optimization
by: Yang, Jiuding, et al.
Published: (2025)

In-Depth and In-Breadth: Pre-training Multimodal Language Models Customized for Comprehensive Chart Understanding
by: Fan, Wan-Cyuan, et al.
Published: (2025)

ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding
by: Kondic, Jovana, et al.
Published: (2026)

AutoCoder: Enhancing Code Large Language Model with \textsc{AIEV-Instruct}
by: Lei, Bin, et al.
Published: (2024)