README.md

---
license: mit
task_categories:
  - question-answering
language:
  - en
tags:
  - math
  - number-sense
  - benchmark
  - shortcuts
  - numerical-reasoning
size_categories:
  - 1K<n<10K
---

# SenseMath: Evaluating Number Sense in Large Language Models

**SenseMath** is a controlled benchmark for measuring whether LLMs can exploit number-sense shortcuts.

## Dataset Description

- **1,600 item families** across 8 categories and 4 digit scales
- **3 variants per family**: strong-shortcut, weak-shortcut, control
- **4,800 total items**
- **Categories**: Magnitude Estimation, Structural Shortcuts, Relative Distance, Cancellation, Compatible Numbers, Landmark Comparison, Equation Reasoning, Option Elimination
- **Digit scales**: d=2, 4, 8, 16

## Files

| File | Description |
|------|-------------|
| `data/sensemath_v2_d2.json` | 400 families, 2-digit operands |
| `data/sensemath_v2_d4.json` | 400 families, 4-digit operands |
| `data/sensemath_v2_d8.json` | 400 families, 8-digit operands |
| `data/sensemath_v2_d16.json` | 400 families, 16-digit operands |
| `data/judge_j1.json` | J1 task: shortcut recognition (251 items) |
| `data/judge_j2.json` | J2 task: strategy identification (80 items) |
| `data/judge_j3.json` | J3 task items |

## Usage

```python
from datasets import load_dataset
ds = load_dataset("DaydreamerMZM/SenseMath", split="train")

# Or load directly
import json
with open("data/sensemath_v2_d4.json") as f:
    families = json.load(f)
```

## Citation

```bibtex
@article{zhuang2025sensemath,
  title={SenseMath: Evaluating Number Sense in Large Language Models},
  author={Zhuang, Haomin and Wang, Xiangqi and Shen, Yili and Cheng, Ying and Zhang, Xiangliang},
  journal={arXiv preprint arXiv:XXXX.XXXXX},
  year={2025}
}
```

## Links

- **Paper**: [arXiv](https://arxiv.org/abs/XXXX.XXXXX)
- **Code**: [GitHub](https://github.com/zhmzm/SenseMath)
- **Project Page**: [zhmzm.github.io/SenseMath](https://zhmzm.github.io/SenseMath/)