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openai.com·14h ago
AI Research
arXiv CS.AI·Jun 10·5 min read
ChartAgent: A Multimodal Agent for Visually Grounded Reasoning in Complex Chart Question Answering
TL;DR
Existing multimodal large language models (LLMs) struggle with unannotated charts that require detailed visual interpretation. ChartAgent is a new framework that performs visual reasoning by breaking down queries into visual subtasks and interacting with chart images.
✦ Why It Matters
Engineers can leverage ChartAgent to improve visual question answering systems for complex data visualizations.
Key Takeaways
- Implement ChartAgent to enhance visual question answering capabilities.
- Utilize specialized actions for effective chart analysis.
- Explore the plug-and-play nature for various LLMs.
- Focus on improving performance for unannotated, complex queries.
Full Summary
Multimodal LLMs have shown potential in answering questions about charts, but their effectiveness diminishes when faced with unannotated charts that require precise visual analysis. ChartAgent is introduced as a novel framework that enhances visual reasoning by decomposing complex queries into manageable visual subtasks.
It employs specialized actions such as drawing annotations, cropping chart regions, and localizing axes, utilizing a library of chart-specific vision tools. This iterative approach mimics human cognitive strategies for understanding charts.
ChartAgent outperforms previous methods, achieving a 16.07% overall accuracy improvement and a 17.31% gain on unannotated, numerically intensive queries across various chart types. Its plug-and-play nature allows it to enhance performance across different underlying LLMs.
This work represents a significant advancement in visually grounded reasoning for chart comprehension.