---
pretty_name: QFT-Bench
language:
- en
license: cc-by-4.0
task_categories:
- question-answering
size_categories:
- n<1K
tags:
- physics
- quantum-field-theory
- high-energy-physics
- reasoning
- benchmark
- llm-evaluation
---

<div align="center">
<h1>LLMs Can Tackle High Energy Theoretical Physics, yet Gaps Remain:<br>Part I, Quantum Field Theory</h1>
<p align="center">
  <a href="https://arxiv.org/abs/2610.33134">📄 Paper</a> |
  <a href="https://huggingface.co/datasets/LLMsHEP/QFT-Bench">🤗 Data</a> 
</p>
</div>

QFT-Bench is a benchmark designed to evaluate the reasoning capabilities of large language models (LLMs) in quantum field theory (QFT), a fundamental theoretical framework underlying modern particle physics.

The benchmark consists of 75 problems covering eight QFT core areas:

- Classical Field Theory (Classical) 
- Quantization of Scalar, Spinor, and Vector Fields (Quantization) 
- Quantum Electrodynamics (QED) 
- Regularization and Renormalization (Renormalization) 
- Non-Abelian Gauge Theory and QCD (Non-Abelian) 
- Electroweak Theory and the Higgs Mechanism (Electroweak) 
- Supersymmetric Field Theories (SUSY) 
- Conformal Field Theories (CFT) 

## Dataset Structure

The dataset is provided in `test.json`. Each problem contains the following fields:

| Field | Description |
|---|---|
| `num` | Problem number |
| `question` | Problem statement |
| `category` | QFT topic category |
| `reference_answer` | Reference final answer |
| `reference_solution` | Reference derivation and solution |
| `numeric_tolerance`, `reference_answer_alt`, `specific_requirement` | Additional evaluation instructions when applicable |

Mathematical expressions are represented using LaTeX.

## Load the dataset

```python
from datasets import load_dataset

dataset = load_dataset("LLMsHEP/QFT-Bench", split="test")

print(f"Number of problems: {len(dataset)}")
print(dataset[0]["question"])
```

## Citation

If you find it useful, feel free to cite our paper (that's an encouragement to us): 
<pre>@article{llmsqft,
  title   = {LLMs Can Tackle High Energy Theoretical Physics, yet Gaps Remain: Part I, Quantum Field Theory},
  author  = {Liu, Tong and Li, Bohan and Cheng, Gongrui and Yu, Zixiong and He, Yulan and Tresp, Volker},
  journal = {arXiv preprint arXiv:2610.33134},
  url     = {https://qft-bench.github.io/},
  year    = {2026}
}</pre>


## License

This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.