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Wisdom of the Silicon Crowd: LLM Ensemble Prediction Capabilities Rival Human Crowd Accuracy
We compare the aggregated LLM predictions on 31 binary questions to that of a crowd of 925 human forecasters from a three-month forecasting tournament.
AI-Augmented Predictions: LLM Assistants Improve Human Forecasting Accuracy
Exploratory analyses showed a pronounced effect in one forecasting item, without which we find that the superforecasting assistant increased accuracy by 43%, compared with 28% for the biased assistant.
Large Language Model Prediction Capabilities: Evidence from a Real-World Forecasting Tournament
Accurately predicting the future would be an important milestone in the capabilities of artificial intelligence.
Diminished Diversity-of-Thought in a Standard Large Language Model
In another, we found that most but not all "correct answers" were robust to changing the order of answer choices.
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