The first thing I'd do if working with an LLM on tabular data is to ask what the best tool would be to work with that data and build up a proper harness to work with the data sensibly. Rawdogging LLM isn't the tool for forecasting like this, as they found.
>We study a frontier LLM in its purest inference regime - a single generation pass over a prompt containing the full training and test data, with no tools, no agentic scaffolding, and no fine-tuning
Sigh. So this is somewhat interesting niche academic research but utterly irrelevant to real-world use cases.
Right. It's kind of like saying gradient boosted trees are bad at text generation. Why would anyone expect them to be good at tabular prediction when they weren't trained to do that?
The first thing I'd do if working with an LLM on tabular data is to ask what the best tool would be to work with that data and build up a proper harness to work with the data sensibly. Rawdogging LLM isn't the tool for forecasting like this, as they found.
>We study a frontier LLM in its purest inference regime - a single generation pass over a prompt containing the full training and test data, with no tools, no agentic scaffolding, and no fine-tuning
Sigh. So this is somewhat interesting niche academic research but utterly irrelevant to real-world use cases.
Unsure if it's LLMs that fail at tabular data or its just that tree boosting are spectacular at that task.
Right. It's kind of like saying gradient boosted trees are bad at text generation. Why would anyone expect them to be good at tabular prediction when they weren't trained to do that?
Just have 2 LLMs debate whether tabs or spaces are the superior choice