Rui-Xi Luo

Two Heads Are Better Than One: Aggregating Weaker LLMs for Better Forecasts

Large language models (LLMs) are increasingly used to forecast real-world events, but access to the strongest individual forecaster may be costly or otherwise constrained. We study weak-to-strong forecast aggregation: can individually weaker LLM forecasters be aggregated to outperform a stronger forecaster? Using Forec...