How Proper Scoring Rules Shape LLM Forecasting
This paper evaluates how reward function choice shapes the performance and behavior of LLM forecasters. We compare five proper scoring rules as training objectives for binary forecasts of resolved real-world events. Although the rules share the same theoretical incentive for truthful probability reporting, the resulting models differ in calibration, probability use, and estimated profiles of…
We haven't written up this one. arXiv cs.AI has the full story — the link below goes straight to it.