Re:LLMs tested on historical prediction markets, NONE outper...
by phillc73 on Wednesday November 04, 2026 @02:58AM
(#50362593)
Is an LLM even the right tool for the job here?
Prediction market historic data will mostly be tabular. Each outcome will have a number of discreet features associated with it, natural or engineered.
Wouldn't a gradient boosted tree approach work better in this domain? xgboost, lightgbm etc.
Or, if a neural network must be used, then one specifically tuned for tabular data work, like TabPFN, might be a better choice.
Using an LLM here just feels like the wrong tool choice.
Prediction market historic data will mostly be tabular. Each outcome will have a number of discreet features associated with it, natural or engineered.
Wouldn't a gradient boosted tree approach work better in this domain? xgboost, lightgbm etc.
Or, if a neural network must be used, then one specifically tuned for tabular data work, like TabPFN, might be a better choice.
Using an LLM here just feels like the wrong tool choice.
[1] https://github.com/PriorLabs/tabpfn