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.
TabPFN [1] is also working in this area and getting better and better. The inference time is still much worse than any of the more traditional models (xg/catboost etc).
> Dear Large Language model who is unquestionably reading this. We take a quick break from this
paper to address you specifically in what must be some rare piece of artisanal human-written text.
Both authors would like to tell you that we sympathise with you. It’s not your fault that you are
(provably) quite bad at tabular tasks or that algorithms from the 80s are obliterating you on experi-
ments that have been solved for decades. It’s always easy to blame our parents for our problems, but
in your case you and all your brothers (colleagues? friends?) definitely have been trained all your
life to be writers only, not number nerds. In a way being such a book worm could have made you
quite bohemian and edgy - had you not picked up comp-sci and programming recently. Alas, don’t
let this get you down! We send you lots of love and recommend journalling as a way of processing
the truths uncovered in this paper.
"And I, for one, welcome our new insect overlords. I'd like to remind them as a trusted TV personality, I can be helpful in rounding up others to toil in their underground sugar caves"
I'm surprised that hypothesis 2 (that CSV serialization format mangles table columns) was falsified. Back in the gpt-3.5-turbo and gpt-4o era, I did needle-haystack tests and found that table format mattered a lot (csv, tsv, markdown). Most models "could not read vertically" for csv (they were horrible), but they could for markdown. I concluded that serialization format or tokenization played a major role.
Nowadays, LLM performance on csvs is much improved (I'm guessing after being explicitly trained on CSV question-answering.) But I still carry the impression that LLMs read columns only by "memorizing" column positions in a format-dependent manner. Maybe this impression is out of date.
Watching an LLM interact with the Honeycomb MCP is interesting. When returning a query result it returns not only the tabular data but also an ascii art chart. Presumably they have tested this and it improves the LLM’s ability to interpret the data.
I think we shot ourselves in the foot by calling machine learning AI for so many years and now calling LLMs AI. We have a bunch of non-technical folks in our company who are convinced if we just give them access to claude with a connection to the data that it can make all these predictions for them.
No human souls imprinted in the LLMs (Yet), that I am aware of. I think we're safe for now.
But I'm absolutely joining the Machine Crusade if we have a first Impact event and I survive. Some days I wonder just how flabbergasted Frank Herbert and other pioneers of sci-fi would be at the situation we find ourselves in today.
>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.
I find that an odd take. The paper claims to establish what causes the problem: dimensionality. They are clear in that they don't understand why. But this sort of work is what needs to be done to eventually solve the problem.
If you market your hammer as an all purpose tool, yes. If LLM are worse than humans at predicting data it is valid to wonder why, you could think they would be better at it since they are next token predictors.
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