Reproduce “FinFlier: Automating Graphical Overlays for Financial Visualizations with Knowledge-Grounding Large Language Model” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Jianing Hao, Manling Yang, Qing Shi, Yuzhe Jiang, Guang Zhang, Wei Zeng
FinFlier is a system that automates the generation of graphical overlays for financial visualizations by employing a knowledge-grounding Large Language Model. The system improves the clarity and engagement of financial narratives, achieving F1 scores up to 0.9455 for text-data binding and receiving high user satisfaction for its enhanced readability and comprehensiveness.
Key insights
- A systematic analysis of 1752 layered charts informed the identification of nine common graphical overlay categories and their effective correspondence with financial narrative structures.
- Knowledge-grounding and advanced prompt engineering are crucial for LLMs to accurately interpret specialized financial vocabulary (e.g., 'bullish trend') and complex narrative structures for text-data binding.
- Graphical overlays, when layered directly onto charts, significantly reduce cognitive load and enhance the understandability, engagement, and comprehensiveness of financial visualizations compared to side-by-side text-chart presentations.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.