FinVis-GPT: A Multimodal Large Language Model for Financial Chart Analysis
FinVis-GPT: A Multimodal Large Language Model for Financial Chart Analysis
This paper introduces FinVis-GPT, a multimodal large language model designed specifically for analyzing financial charts. By integrating visual and textual data through a multimodal architecture, the model leverages instruction tuning to align with financial analysis tasks, such as interpreting charts, generating descriptions, answering questions, and predicting market trends. A key contribution is the creation of a financial task-oriented dataset, which includes diverse financial charts and their descriptions, used for pre-training and fine-tuning the model. This dataset is intended for public release to foster further research in the field.
The methodology involves training FinVis-GPT on this generated dataset, employing techniques like instruction tuning to enhance its ability to handle complex financial queries. Evaluation through case studies demonstrates that FinVis-GPT surpasses existing multimodal LLMs in performance, showing effectiveness in various chart-related tasks. The results highlight its potential as a tool for financial professionals and researchers, offering accurate analysis and predictions. Overall, FinVis-GPT represents a pioneering application of multimodal LLMs in finance, with implications for automating and improving financial data interpretation.
Highlights
- 1Proposes FinVis-GPT, a novel multimodal large language model tailored for financial chart analysis
- 2Leverages instruction tuning and multimodal capabilities to interpret financial charts and provide analysis
- 3Generates a financial task-oriented dataset for pre-training alignment and instruction tuning
- 4Demonstrates superior performance in tasks like generating descriptions, answering questions, and predicting market trends
- 5Serves as a pioneering effort in applying multimodal LLMs to finance and plans to release the dataset publicly
Methods
- MMultimodal large language model (LLM) architecture integrating visual and textual data
- MInstruction tuning to align the model with financial analysis tasks
- MDataset generation comprising various financial charts and corresponding descriptions for training
- MEvaluation through case studies to assess performance on financial chart-related tasks
Results
- RFinVis-GPT outperforms existing state-of-the-art multimodal LLMs in financial chart analysis tasks
- RThe model effectively generates accurate descriptions and answers questions based on financial charts
- RShows promising capability in predicting future market trends from chart data
- RThe generated dataset supports model training and is planned for public release to accelerate research
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