Reproduce “FinVis-GPT: A Multimodal Large Language Model for Financial Chart Analysis” 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
Ziao Wang, Yuhang Li, Junda Wu, Jaehyeon Soon, Xiaofeng Zhang
FinVis-GPT, a multimodal large language model, enhances financial chart analysis by generating accurate descriptions, answering specific questions, and predicting future trends. The model leverages a newly created financial task-oriented dataset to achieve improved performance compared to general-purpose multimodal LLMs.
Key insights
- General-purpose multimodal LLMs frequently exhibit misinterpretations or "hallucinations" when confronted with domain-specific visual data, such as financial charts.
- Tailoring multimodal LLMs with a purpose-built, large-scale dataset significantly improves their accuracy, relevance, and predictive capabilities within a specialized domain like financial analysis.
- A combination of pre-training alignment and instruction tuning with high-quality, domain-specific data enables an LMM to achieve human-like interpretation and sophisticated analytical functions, including future trend prediction.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.