Upload README.md

README.md

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-license: cc-by-4.0
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+<h1>
+LLMs Can Tackle High Energy Theoretical Physics, but Gaps Remain:<br>
+Part I, Quantum Field Theory
+</h1>
+
+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 |
+| `answer` | Reference final answer |
+| `solution` | Reference derivation and solution |
+| `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): 
+
+
+
+## License
+
+This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.