nvAgent: Automated Data Visualization from Natural Language via Collaborative Agent Workflow
Geliang Ouyang, Jingyao Chen, Zhihe Nie, Yi Gui, Yao Wan, Hongyu, Zhang, Dongping Chen

TL;DR
nvAgent is a collaborative multi-agent system that converts natural language descriptions into data visualizations, effectively handling complex queries across multiple tables and outperforming existing methods.
Contribution
The paper introduces nvAgent, a novel multi-agent framework that enhances NL2Vis by improving reasoning and visualization generation from complex, multi-table data sources.
Findings
Achieves 7.88% improvement on single-table visualization tasks
Achieves 9.23% improvement on multi-table visualization tasks
Maintains nearly 20% performance margin over previous models
Abstract
Natural Language to Visualization (NL2Vis) seeks to convert natural-language descriptions into visual representations of given tables, empowering users to derive insights from large-scale data. Recent advancements in Large Language Models (LLMs) show promise in automating code generation to transform tabular data into accessible visualizations. However, they often struggle with complex queries that require reasoning across multiple tables. To address this limitation, we propose a collaborative agent workflow, termed nvAgent, for NL2Vis. Specifically, nvAgent comprises three agents: a processor agent for database processing and context filtering, a composer agent for planning visualization generation, and a validator agent for code translation and output verification. Comprehensive evaluations on the new VisEval benchmark demonstrate that nvAgent consistently surpasses state-of-the-art…
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Code & Models
Videos
Taxonomy
TopicsSemantic Web and Ontologies
