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.
That reminds me of a story--probably an HN submission--where someone tuned a model on historical data which got insanely close to predicting exactly what would happen every day. The "real" vs "predicted" graphs were almost indistinguishable.
Unfortunately, the reason they were so similar was that it decided each day's stock prices would be the same as the end of yesterday, which was on some measures very close yet also useless for prophetic profits.
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.
[1] https://github.com/PriorLabs/tabpfn
That reminds me of a story--probably an HN submission--where someone tuned a model on historical data which got insanely close to predicting exactly what would happen every day. The "real" vs "predicted" graphs were almost indistinguishable.
Unfortunately, the reason they were so similar was that it decided each day's stock prices would be the same as the end of yesterday, which was on some measures very close yet also useless for prophetic profits.
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