QFT-Bench / README.md
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---
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.