DeepVIS: Bridging Natural Language and Data Visualization Through Step-wise Reasoning
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces Chain-of-Thought (CoT) reasoning to improve Natural Language to Visualization (NL2VIS) systems. The CoT framework enhances visualization quality and provides transparent reasoning for better user control and refinement.
Area Of Science
- Computer Science
- Data Visualization
- Artificial Intelligence
Background
- Data visualization is crucial for insight discovery but requires expertise and can disrupt analysis workflows.
- Current Natural Language to Visualization (NL2VIS) systems lack transparency, hindering user understanding and refinement of generated visualizations.
Purpose Of The Study
- To integrate Chain-of-Thought (CoT) reasoning into the NL2VIS pipeline to enhance transparency and control.
- To develop methods for creating and utilizing CoT reasoning steps in NL2VIS for improved visualization generation.
Main Methods
- Designed a comprehensive CoT reasoning process for NL2VIS and an automatic pipeline for dataset annotation.
- Introduced nvBench-CoT, a dataset with detailed reasoning steps for fine-tuning NL2VIS models.
- Developed DeepVIS, an interactive interface for inspecting and adjusting CoT reasoning in visualization generation.
Main Results
- The CoT framework significantly enhances NL2VIS quality, achieving state-of-the-art performance with the nvBench-CoT dataset.
- Quantitative benchmarks, use cases, and user studies validate the effectiveness of the CoT approach.
- DeepVIS enables users to understand, debug, and refine visualization outputs by interacting with reasoning steps.
Conclusions
- Integrating CoT reasoning into NL2VIS pipelines offers a transparent and controllable approach to automated visualization.
- The proposed framework improves the quality and interpretability of visualizations generated from natural language descriptions.
- This work paves the way for more user-centric and effective automated data visualization tools.
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