# BIY: Preparing a Dataset and Benchmarking AI Models for Scatterplot-Related Tasks

DevFeed: [BIY: Preparing a Dataset and Benchmarking AI Models for Scatterplot-Related Tasks](<https://devfeed.tech/articles/biy-preparing-a-dataset-and-benchmarking-ai-models-for-scatterplot-related-tasks-26294.md>)

Original publisher: [Read original article](<https://medium.com/feedzaitech/biy-preparing-a-dataset-and-benchmarking-ai-models-for-scatterplot-related-tasks-11cbef120cd1?source=rss----e11168e7fe6b---4>)

Author: João Palmeiro

Published: 2026-01-19T14:52:55Z

Content type: article

Language: en

Sources: [Feedzai](<https://devfeed.tech/sources/feedzai.md>)

Topics: [Accessibility](<https://devfeed.tech/topics/accessibility.md>), [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>), [multimodal-ai](<https://devfeed.tech/topics/multimodal-ai.md>), [Canvas](<https://devfeed.tech/topics/canvas.md>), [AI Models](<https://devfeed.tech/topics/ai-models.md>), [dataset](<https://devfeed.tech/topics/dataset.md>), [SVG](<https://devfeed.tech/topics/svg.md>)

Tags: [accessibility](<https://devfeed.tech/tags/accessibility.md>), [ai-models](<https://devfeed.tech/tags/ai-models.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [benchmarking](<https://devfeed.tech/tags/benchmarking.md>), [canvas](<https://devfeed.tech/tags/canvas.md>), [clustering](<https://devfeed.tech/tags/clustering.md>), [data-visualization](<https://devfeed.tech/tags/data-visualization.md>), [dataset](<https://devfeed.tech/tags/dataset.md>), [large-language-models](<https://devfeed.tech/tags/large-language-models.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [multimodal-ai](<https://devfeed.tech/tags/multimodal-ai.md>), [outlier-detection](<https://devfeed.tech/tags/outlier-detection.md>), [svg](<https://devfeed.tech/tags/svg.md>)

## AI overview

The article introduces Benchmark It Yourself (BIY), an effort to prepare a dataset and benchmark multimodal AI models for scatterplot-related tasks. It examines generating distribution-aware descriptions from scatterplot images to improve the accessibility of canvas charts, including direct description generation and structured-data extraction for predefined templates. Initial results were mixed.

## Source excerpt

Benchmark It Yourself (BIY): Preparing a Dataset and Benchmarking AI Models for Scatterplot-Related Tasks When we need to visualize and interact with millions, or even just thousands, of individual points while analyzing data, we typically resort to rendering them in the browser using a canvas. The other common approach for the web, SVG, doesn't scale when the number of individual elements increases to such quantities. However, while solving one problem, canvas charts introduce a new challenge: accessibility. Although SVG charts are not accessible by default, they can be by design. Each part of an SVG chart has a corresponding element on the web page, allowing for a programmable, accessible experience for screen reader users. We can simply think of SVG as HTML. On the other hand, a canvas chart is just like a PNG image. If a screen reader user tries to learn more about a canvas chart, unless the developer has prepared a detailed description of it, they will just hear the word "image". There's no way to get an idea of what one of these charts represents, let alone extract any insights. For static charts, the solution can be as simple as preparing a description and integrating it into the rendered chart. However, for platforms leveraging dynamic, large datasets, automatically generating these descriptions is not a simple task, especially for charts like scatterplots where data distributions can assume countless forms. At Feedzai, we started exploring ways to generate data distribution-aware descriptions for scatterplots from their respective images using recent multimodal AI models. When the raw data is not available, or the datasets are composed of several thousand or million instances, relying on chart images and these models becomes tempting. This combination has the potential to generate such descriptions and serve them alongside their respective charts, significantly improving the accessibility of canvas charts. That said, we focused on two main directions: using