[simonw]

I don't think tabular data of any sort is a particularly good fit for LLMs at the moment. What are you trying to do with it?

If you want to answer questions like "how many students does Everglade High School have?" and you have a spreadsheet of schools where one of the columns is "number of students" I guess you could feed that into an LLM, but it doesn't feel like a great tool for the job.

I'd instead use systems like ChatGPT Code Interpreter where the LLM gets to load up that data programatically and answer questions by running code against it. Text-to-SQL systems could work well for that too.

[btown]

This is an active area of research: https://github.com/SpursGoZmy/Awesome-Tabular-LLMs is a good starting point!

[cosmie]

For me personally, a lot of times it's for table augmentation purposes. Appending additional columns to a dataset, such as a cleaned/standardized version of another field, extracting a value from another field, or appending categorization attributes (sometimes pre-seeded and sometimes just giving it general direction).

Or sometimes I'll manually curate a field like that, and then ask it to generate an Excel function that can be used to produce as similar a result as possible for automated categorization in the future.

So in most cases I both want to provide it with tabular data, and also want tabular data back out. In general I've gotten decent results for these sorts of use cases, but when it falls down it's almost always addressable by tinkering with the formatting related instructions – sometimes by tweaking the input and sometimes by tweaking the instructions for the desired output.