PaperLedge

Computer Vision - FinMMR Make Financial Numerical Reasoning More Multimodal, Comprehensive, and Challenging

Listen Later

Hey PaperLedge learning crew, Ernis here, ready to dive into some fascinating research! Today, we're tackling a paper that looks at how well AI can understand the complex world of finance, especially when dealing with numbers, charts, and financial reports. Think of it like this: can AI become a savvy financial analyst?

The researchers created a new test, called FinMMR, to really push AI models to their limits. Now, there are already tests out there, but this one's special because it focuses on a few key things:

  • Multimodality: This isn't just about reading text. It's about understanding text and images together. Imagine trying to understand a company's performance by reading their annual report and looking at the charts showing their sales. The AI has to do both! They took existing financial questions and added tons of visuals from actual Chinese financial research reports. We're talking over 4,300 questions and almost 9,000 images!
  • Comprehensiveness: This test covers a LOT of ground in the finance world. It's not just about one area like stocks. It covers 14 different financial areas like corporate finance, banking, and even analyzing entire industries. It’s like giving the AI a crash course in all things money!
  • Challenge: This is the real kicker. The questions aren't easy! The AI needs to do multi-step reasoning, meaning it has to combine financial knowledge with what it sees in the images and reads in the text to get the right answer. It's like solving a complex puzzle where you need to understand both the picture on the box and the instructions.

    • Think of it like teaching a robot to understand the stock market. You can't just feed it numbers; it needs to understand the stories behind the numbers, the charts that visualize the trends, and the reports that explain the details.

    So, how well did the AI models do? Well, even the best AI only got about 53% accuracy on the hardest questions. That might sound okay, but in the financial world, even small errors can have big consequences. This shows there's still a lot of room for improvement!

    "The best-performing MLLM achieves only 53.0% accuracy on Hard problems."

    Why does this matter? Well, imagine having AI that can accurately analyze financial data, predict market trends, and help us make smarter investment decisions. This research is a step towards that future. It could help:

    • Investors: Make more informed decisions.
    • Financial analysts: Free up their time to focus on more complex tasks.
    • Regulators: Better monitor the financial markets and prevent fraud.

      • This FinMMR benchmark helps researchers understand the limits of existing AI models and provides a clear target for future development. It’s about building AI that can not only process information but also reason about it in a sophisticated and nuanced way.

      Now, a few questions that pop into my head as I'm thinking about this:

      • How could biases in the training data used to create these AI models affect their performance and potentially lead to unfair or inaccurate financial analyses?
      • What are the ethical considerations of using AI in financial decision-making, especially when it comes to transparency and accountability? If an AI makes a bad investment decision, who is responsible?

        • What do you think, learning crew? Could AI become our next top financial advisor? Let's discuss!



        Credit to Paper authors: Zichen Tang, Haihong E, Jiacheng Liu, Zhongjun Yang, Rongjin Li, Zihua Rong, Haoyang He, Zhuodi Hao, Xinyang Hu, Kun Ji, Ziyan Ma, Mengyuan Ji, Jun Zhang, Chenghao Ma, Qianhe Zheng, Yang Liu, Yiling Huang, Xinyi Hu, Qing Huang, Zijian Xie, Shiyao Peng
        ...more
        View all episodesView all episodes
        Download on the App Store
        PaperLedgeBy ernestasposkus