To accelerate and refine decision-making in a fast-paced, global marketplace, enterprises may deploy generative artificial intelligence models to help summarize and interpret the charts that often fill market summaries and financial reports.

But even the latest vision-language models sometimes struggle with this task, since it requires a model to integrate visual, numerical, and linguistic understanding. A company that invests in a state-of-the-art model might still receive inaccurate or incomplete information.

To fill this performance gap, researchers from MIT and the MIT-IBM Computing Research Lab developed a multifaceted resource for AI users that is specifically designed to teach vision-language models (VLMs) how to effectively interpret charts. 

They used a novel data generation method to build a state-of-the-art dataset that includes more than a million varied charts. The dataset also encodes many visual, linguistic, and numerical components of each chart image, which enable models to robustly reason about the information in a chart.

The researchers used this dataset, called ChartNet, to train a series of open-source VLMs.  Many of these smaller models significantly outperformed orders of magnitude larger, commercial models on tasks like data extraction and chart summarization.

By enabling open-source models to outperform their commercial counterparts, ChartNet could allow small firms with limited budgets to more readily utilize AI. The open-source dataset can be used to improve the capabilities of AI models for tasks like business trend analysis and scientific figure interpretation.

“We developed ChartNet to be a one-stop shop for chart understanding, covering basically anything that an AI model and a practitioner who is training that model might need.

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