Update README.md

README.md

@@ -16,8 +16,8 @@ language:
 
 This dataset contains single-ID forecasting questions derived from the [ForecastBench project](https://github.com/forecastingresearch/forecastbench-datasets). It includes two configurations:
 
-- `forecastbench_single_questions_2024-12-08`: Contains 500 binary forecasting questions with resolved real-world outcomes.
-- `forecastbench_single_questions_with_human_2024-07-21`: The same 500 questions, augmented with human forecast probabilities from public and superforecaster groups.
+- `forecastbench_single_questions_2024-12-08`: Contains 429 forecasting questions with resolved real-world outcomes.
+- `forecastbench_single_questions_with_human_2024-07-21`: Contains 473 questions with resolved real-world outcomes, augmented with human forecast probabilities from public and superforecaster groups.
 
 ## Dataset Structure
 
@@ -28,8 +28,8 @@ Each entry in the dataset is a JSON object with the following fields:
 - `background`: Additional context or background information.
 - `source`: Origin of the question (e.g., "manifold").
 - `answer`: Binary outcome (0 or 1) representing the resolved real-world result.
-- `human_public_forecast`: (Optional) Forecast probability from the public group.
-- `human_super_forecast`: (Optional) Forecast probability from the superforecaster group.
+- `human_public_forecast`: (Optional) Forecast probability made by the public human group.
+- `human_super_forecast`: (Optional) Forecast probability made by the superforecaster human group.
 
 ## Usage
 
@@ -56,13 +56,12 @@ If you use this dataset, please cite the original ForecastBench project and prov
 ## Citation
 
 ```
-@misc{forecastbench2023,
-  author = {Forecasting Research Team},
-  title = {ForecastBench: A Benchmark Dataset for Forecasting},
-  year = {2023},
-  publisher = {GitHub},
-  journal = {GitHub repository},
-  howpublished = {\url{https://github.com/forecastingresearch/forecastbench-datasets}}
+@inproceedings{karger2025forecastbench,
+      title={ForecastBench: A Dynamic Benchmark of AI Forecasting Capabilities},
+      author={Ezra Karger and Houtan Bastani and Chen Yueh-Han and Zachary Jacobs and Danny Halawi and Fred Zhang and Philip E. Tetlock},
+      year={2025},
+      booktitle={International Conference on Learning Representations (ICLR)},
+      url={https://iclr.cc/virtual/2025/poster/28507}
 }
 ```