Hey PaperLedge crew, Ernis here, ready to dive into some seriously fascinating research! Today, we're talking about how well Large Language Models – those AI brains that power things like ChatGPT – are doing at something super important: creating structured data.

Think of it like this: you ask an LLM to build you a website. It needs to not just write the words, but also create the behind-the-scenes code, like HTML, that tells the browser how to display everything. Or imagine asking it to organize your expenses into a spreadsheet – it needs to create a properly formatted CSV file. Getting that structure right is crucial.

Now, researchers have created something called StructEval – a brand new "report card" for LLMs, specifically focused on how well they handle different types of structured data. This isn't just about generating text; it's about producing accurate and usable code and data formats.

StructEval throws all sorts of challenges at these LLMs. It tests them in two main ways:

They're testing the models on a whopping 18 different formats – from everyday things like JSON and CSV to more complex stuff like HTML, React code, and even SVG images!

So, how are these LLMs doing? Well, the results are… interesting. Even the super-smart models aren't perfect. The best one tested, called o1-mini, only managed an average score of about 75%. Open-source alternatives are even further behind. Yikes!

"We find generation tasks more challenging than conversion tasks, and producing correct visual content more difficult than generating text-only structures."

That means it's harder for them to create a structure from scratch than it is to translate between existing structures. And, unsurprisingly, getting the visual stuff right (like creating a working SVG image) is tougher than just generating text-based data.

Why does this matter? Well, for developers, this tells us which LLMs are reliable for generating code and data. For businesses, it highlights the potential (and limitations) of using AI to automate tasks like data entry, report generation, and website design. And for everyone, it's a reminder that even the most advanced AI still has room to improve.

Think about it: if an LLM can't reliably generate structured data, it limits its usefulness in all sorts of applications. We rely on the structure of data for everything from analyzing scientific results to managing our finances.

So, here are a couple of things that are bouncing around in my head after reading this:

Let me know what you think, PaperLedge crew. This is Ernis, signing off. Keep learning!