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FinFlier: Automating Graphical Overlays for Financial Visualizations With Knowledge-Grounding Large Language Model
Automating graphical overlays for financial visualizations is now feasible. FinFlier, a novel system using large language models, effectively generates context-rich overlays for financial narratives.
Area of Science:
- Data Visualization
- Financial Analytics
- Natural Language Processing
Background:
- Graphical overlays enhance financial narrative visualizations by adding context.
- Automating these overlays is difficult due to complex narratives and unclear design principles.
Purpose of the Study:
- To develop an automated system for generating graphical overlays in financial visualizations.
- To address the challenges of complex narrative structures and effective overlay design.
Main Methods:
- Surveyed 1752 layered charts to identify common overlays and narrative structures.
- Designed FinFlier, a two-stage system using a knowledge-grounding large language model.
- Employed prompt engineering for text-data binding and narrative sequencing for graphics generation.
Main Results:
- Developed FinFlier, an innovative system for automating graphical overlays.
- Demonstrated system feasibility and expressiveness through a diverse gallery of financial visualizations.
- User studies confirmed the effectiveness and quality of generated layered charts.
Conclusions:
- FinFlier successfully automates the creation of effective graphical overlays for financial narratives.
- The system enhances the connection between financial data and textual context.
- Automated overlays improve the clarity and insightfulness of financial visualizations.
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