FinFlier: Automating Graphical Overlays for Financial Visualizations with Knowledge-Grounding Large Language Model

TL;DR

FinFlier is a system that uses a knowledge-grounding large language model to automate the creation of graphical overlays in financial visualizations, improving clarity and narrative communication.

Contribution

The paper introduces FinFlier, a novel two-stage system that leverages large language models and prompt engineering to automate graphical overlays in financial charts.

Findings

Effective overlays generated for diverse financial visualizations

System outperforms baseline methods in user studies

High-quality overlays enhance narrative clarity

Abstract

Graphical overlays that layer visual elements onto charts, are effective to convey insights and context in financial narrative visualizations. However, automating graphical overlays is challenging due to complex narrative structures and limited understanding of effective overlays. To address the challenge, we first summarize the commonly used graphical overlays and narrative structures, and the proper correspondence between them in financial narrative visualizations, elected by a survey of 1752 layered charts with corresponding narratives. We then design FinFlier, a two-stage innovative system leveraging a knowledge-grounding large language model to automate graphical overlays for financial visualizations. The text-data binding module enhances the connection between financial vocabulary and tabular data through advanced prompt engineering, and the graphics overlaying module generates effective overlays with narrative sequencing. We demonstrate the feasibility and expressiveness of FinFlier through a gallery of graphical overlays covering diverse financial narrative visualizations. Performance evaluations and user studies further confirm system's effectiveness and the quality of generated layered charts.