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--- |
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pretty_name: ForecastBench Single Questions |
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license: cc-by-sa-4.0 |
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tags: |
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- forecasting |
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- calibration |
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- real-world forecasts |
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- human forecasts |
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- cc-by-sa-4.0 |
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task_categories: |
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- question-answering |
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- table-question-answering |
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- text-generation |
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language: |
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- en |
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configs: |
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- config_name: forecastbench_single_questions_2024-12-08 |
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data_files: forecastbench_single_questions_2024-12-08.jsonl |
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- config_name: forecastbench_single_questions_human_2024-07-21 |
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data_files: forecastbench_single_questions_human_2024-07-21.jsonl |
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size_categories: |
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- n<1K |
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--- |
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# ForecastBench Single Questions |
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This dataset contains single-ID forecasting questions derived from the [ForecastBench project](https://github.com/forecastingresearch/forecastbench-datasets). It includes two configurations: |
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- `forecastbench_single_questions_2024-12-08`: Contains 429 forecasting questions with resolved real-world outcomes. |
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- `forecastbench_single_questions_human_2024-07-21`: Contains 473 questions with resolved real-world outcomes, augmented with human forecast probabilities from public and superforecaster groups. |
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## Dataset Structure |
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Each entry in the dataset is a JSON object with the following fields: |
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- `id`: Unique identifier for the question. |
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- `question`: The forecasting question text. |
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- `background`: Additional context or background information. |
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- `source`: Origin of the question (e.g., "manifold"). |
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- `source_intro`: A standardized prompt prefix shown to forecasters, e.g."We would like you to predict the outcome of a prediction market. A prediction market, in this context, is the aggregate of predictions submitted by users on the website Manifold. You’re going to predict the probability that the market will resolve as ‘Yes’." |
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- `answer`: Binary outcome (0 or 1) representing the resolved real-world result. |
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- `human_public_forecast`: (Optional) Forecast probability made by the public human group. |
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- `human_super_forecast`: (Optional) Forecast probability made by the superforecaster (expert) human group. |
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## Usage |
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To load a specific configuration using the Hugging Face `datasets` library: |
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```python |
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from datasets import load_dataset |
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# Load the base dataset without human forecasts |
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dataset = load_dataset("Duruo/forecastbench-single_question", name="forecastbench_single_questions_2024-12-08") |
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# Load the dataset with human forecasts |
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dataset_with_human = load_dataset("Duruo/forecastbench-single_question", name="forecastbench_single_questions_human_2024-07-21") |
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``` |
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## License and Attribution |
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This dataset is released under the [Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/). |
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It is based on the original [ForecastBench dataset](https://github.com/forecastingresearch/forecastbench-datasets), which is also licensed under CC BY-SA 4.0. |
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If you use this dataset, please cite the original ForecastBench project and provide appropriate attribution. |
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## Citation |
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``` |
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@inproceedings{karger2025forecastbench, |
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title={ForecastBench: A Dynamic Benchmark of AI Forecasting Capabilities}, |
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author={Ezra Karger and Houtan Bastani and Chen Yueh-Han and Zachary Jacobs and Danny Halawi and Fred Zhang and Philip E. Tetlock}, |
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year={2025}, |
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booktitle={International Conference on Learning Representations (ICLR)}, |
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url={https://iclr.cc/virtual/2025/poster/28507} |
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} |
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``` |
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``` |
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:contentReference[oaicite:21]{index=21} |
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--- |
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:contentReference[oaicite:23]{index=23} :contentReference[oaicite:26]{index=26}:contentReference[oaicite:28]{index=28} |
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::contentReference[oaicite:29]{index=29} |
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``` |
