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--- |
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license: mit |
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task_categories: |
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- question-answering |
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- text-generation |
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language: |
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- en |
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tags: |
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- math |
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- reasoning |
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- synthetic |
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size_categories: |
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- 1K<n<10K |
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--- |
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# GSM-DC Test Dataset |
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This dataset contains the test set for GSM-DC (Grade School Math with Distractor Chains), a synthetic math reasoning dataset with controlled complexity. |
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## Dataset Details |
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- **Total Problems**: 6300 |
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- **Operation Counts (OP)**: 16-22 (out-of-distribution test set) |
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- **Problem Types**: Graph-based mathematical reasoning problems |
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- **Noise Levels**: Light, Medium, Hard (distractor difficulty) |
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## Dataset Structure |
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Each problem in `all_problems.json` contains: |
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- `problem_text`: The problem statement with all variables and relationships |
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- `solution`: Step-by-step ground truth solution |
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- `final_answer`: The numerical answer |
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- `n_op`: Number of operations (16-22) |
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- `noise_level`: Distractor difficulty (light/medium/hard) |
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- `graph_structure`: Internal graph representation |
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- `template_id`: Problem template identifier |
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## Usage |
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```python |
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import json |
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# Load the dataset |
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with open('all_problems.json', 'r') as f: |
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problems = json.load(f) |
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# Access a problem |
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problem = problems[0] |
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print(problem['problem_text']) |
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print(problem['solution']) |
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print(problem['final_answer']) |
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``` |
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## Citation |
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If you use this dataset, please cite: |
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```bibtex |
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@inproceedings{gsm-dc-2025, |
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title={GSM-DC: Grade School Math with Distractor Chains}, |
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author={[Your Name]}, |
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booktitle={Proceedings of EMNLP 2025}, |
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year={2025} |
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} |
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``` |
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## Paper |
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Published at EMNLP 2025. [Paper Link] |
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## License |
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MIT License |
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