README.md

---
dataset_info:
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    dtype: string
  - name: question
    dtype: string
  - name: answer
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  - name: constraint_desc
    list: string
  - name: key
    dtype: string
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  - name: dev
    num_bytes: 123575
    num_examples: 90
  - name: test
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    num_examples: 332
  download_size: 223359
  dataset_size: 601879
configs:
- config_name: default
  data_files:
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    path: data/dev-*
  - split: test
    path: data/test-*
license: apache-2.0
language:
- en
size_categories:
- n<1K
---



# Math-IF Dataset Card

## Dataset Description

Math-IF (MathIF) is an instruction-following benchmark built on top of math word problems. Each example includes a math question together with explicit, verifiable instructions about how the model should respond (e.g., format, style, or structural constraints). The benchmark is designed to jointly test:

- instruction following in the **reasoning trace (RT)** and  
- instruction following and correctness in the **final answer (FA)**.

In this repository, Math-IF is used as both a development set and a test benchmark for controllable reasoning models.

## Intended Use

- Evaluate how well models follow explicit instructions when solving math problems.


The dataset is intended for **research and benchmarking** only.

## Dataset Structure

From the accompanying paper in this repository (see `paper/`):

- **Size**:
  - **Dev**: 90 examples  
  - **Test**: 332 examples  
- **Splits used here**:
  - The **GSM8K partition** is used as dev set for model selection.
  - The remaining partition is used as test set.

Each instance conceptually includes:

- **`prompt`**: the user prompt with the math question and instruction.
- **`answer`**: the ground-truth final answer.
- **`question`**: the underlying math word problem (without instructions).
- **metadata for evaluation**: information needed to compute instruction-following metrics and answer accuracy.

## Tasks and Evaluation

- **Main task**: Instruction-following on math problems.
- **Metrics**:
  - *Instruction-level loose-accuracy* (as defined in the Math-IF paper) for both RTs and FAs, yielding **IF-RT** and **IF-FA**.
  - **Answer accuracy** measuring whether the final numeric answer is correct.

## Data Source

Math-IF was introduced to study the trade-off between reasoning performance and instruction-following in large reasoning models. For complete details, examples, and official evaluation scripts, please see the original Math-IF paper and repository.

## License

- **License**: Apache 2.0  

## Known Limitations and Considerations

- The dataset focuses on **math word problems**, so instruction-following performance may differ on other domains (e.g., open-ended dialogue, code generation).
- The benchmark size is modest (422 examples total in the dev+test configuration used here), which can make very fine-grained comparisons noisy.
- Instructions are in **English**, so the benchmark does not directly evaluate multilingual behavior.

## Citation


```bibtex
@article{fu2025scaling,
  title={Scaling Reasoning, Losing Control: Evaluating Instruction Following in Large Reasoning Models},
  author={Fu, Tingchen and Gu, Jiawei and Li, Yafu and Qu, Xiaoye and Cheng, Yu},
  journal={arXiv preprint arXiv:2505.14810},
  year={2025}
}
```