README.md

metadata

language:
  - en
license: apache-2.0
size_categories:
  - n<1K
task_categories:
  - text-generation
dataset_info:
  features:
    - name: source
      dtype: string
    - name: question
      dtype: string
    - name: answer
      dtype: string
    - name: constraint_desc
      list: string
    - name: key
      dtype: string
    - name: instruction_id_list
      list: string
    - name: kwargs
      list:
        - name: capital_frequency
          dtype: int64
        - name: capital_relation
          dtype: string
        - name: num_words
          dtype: int64
        - name: relation
          dtype: string
        - name: keyword
          dtype: string
        - name: frequency
          dtype: int64
        - name: prompt_to_repeat
          dtype: string
        - name: keywords
          list: string
        - name: forbidden_words
          list: string
        - name: num_highlights
          dtype: int64
        - name: end_phrase
          dtype: string
        - name: num_bullets
          dtype: int64
        - name: section_spliter
          dtype: string
        - name: num_sections
          dtype: int64
        - name: language
          dtype: string
    - name: prompt
      dtype: string
  splits:
    - name: dev
      num_bytes: 123575
      num_examples: 90
    - name: test
      num_bytes: 478304
      num_examples: 332
  download_size: 223359
  dataset_size: 601879
configs:
  - config_name: default
    data_files:
      - split: dev
        path: data/dev-*
      - split: test
        path: data/test-*

Math-IF Dataset Card

This dataset is associated with the paper From Leaky Thoughts to Private Reasoning: Controlling What LRMs Say to Themselves.

The official code repository for the project is available here: UKPLab/arxiv2026-controllable-reasoning-models.

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:

• 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

@misc{puerto2026controllablereasoningmodelsprivate,
      title={Controllable Reasoning Models Are Private Thinkers}, 
      author={Haritz Puerto and Haonan Li and Xudong Han and Timothy Baldwin and Iryna Gurevych},
      year={2026},
      eprint={2602.24210},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2602.24210}, 
}

@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}
}