LLM Forecasting for Future Prediction
Published:Dec 31, 2025 18:59
•1 min read
•ArXiv
Analysis
This paper addresses the critical challenge of future prediction using language models, a crucial aspect of high-stakes decision-making. The authors tackle the data scarcity problem by synthesizing a large-scale forecasting dataset from news events. They demonstrate the effectiveness of their approach, OpenForesight, by training Qwen3 models and achieving competitive performance with smaller models compared to larger proprietary ones. The open-sourcing of models, code, and data promotes reproducibility and accessibility, which is a significant contribution to the field.
Key Takeaways
- •Addresses the challenge of future prediction using language models.
- •Synthesizes a large-scale forecasting dataset from news events.
- •Achieves competitive performance with smaller models compared to larger proprietary ones.
- •Open-sources models, code, and data for reproducibility and accessibility.
Reference
“OpenForecaster 8B matches much larger proprietary models, with our training improving the accuracy, calibration, and consistency of predictions.”