This documentation provides an overview of the raw dataset utilized in our research paper, Approaching Human-Level Forecasting with Language Models, authored by Danny Halawi, Fred Zhang, Chen Yueh-Han, and Jacob Steinhardt.
The dataset originates from forecasting platforms such as Metaculus, Good Judgment Open, INFER, Polymarket, and Manifold. These platforms engage users in predicting the likelihood of future events by assigning probabilities to various outcomes. The data structure encompasses:
Forecasts can be submitted any time between the begin date and the earlier of the resolve date or close date. Refer to Table 1 in the paper for a detailed example of these fields in action.
Our dataset aggregates forecasting questions from the aforementioned platforms, resulting in a comprehensive collection of:
The questions cover a broad spectrum of topics worldwide, providing a diverse and extensive dataset for forecasting analysis.
This dataset plays a crucial role in our study, enabling us to explore the capabilities of language models in forecasting and their potential to achieve human-level performance in predicting future events.
For more details on our methodology and findings, please refer to our paper linked at the beginning of this document.
If you find our dataset and research useful for your work, please cite it using the following BibTeX entry:
```bibtex @misc{halawi2024approaching, title={Approaching Human-Level Forecasting with Language Models}, author={Danny Halawi and Fred Zhang and Chen Yueh-Han and Jacob Steinhardt}, year={2024}, eprint={2402.18563}, archivePrefix={arXiv}, primaryClass={cs.LG} }