Duplicate from nvidia/ProfBench

Co-authored-by: Zhilin Wang <zhilinw@users.noreply.huggingface.co>

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README.md

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+---
+license: other
+license_name: nvidia-evaluation-dataset-license
+language:
+- en
+pretty_name: ProfBench
+size_categories:
+- n<1K
+tags:
+- human-feedback
+- chemistry
+- physics
+- consulting
+- finance
+---
+## Dataset Description:
+
+[Leaderboard](https://huggingface.co/spaces/nvidia/ProfBench) | [Blog](https://huggingface.co/blog/nvidia/profbench) | [Paper](https://arxiv.org/abs/2510.18941) | [Data](https://huggingface.co/datasets/nvidia/ProfBench) | [Code](https://github.com/NVlabs/ProfBench) | [Nemo Evaluator SDK](https://github.com/NVIDIA-NeMo/Evaluator)
+
+[![Watch the video](https://img.youtube.com/vi/GEPvdq3C54s/maxresdefault.jpg)](https://www.youtube.com/watch?v=GEPvdq3C54s)
+
+More than 3000 rubric criteria across 40 human-annotated tasks presenting reports addressing professional tasks across PhD STEM (Chemistry, Physics) and Professional Services (Financial Services, Management Consulting) domains. 
+
+This dataset is ready for commercial/non-commercial use.
+
+## Dataset Owner(s):
+NVIDIA Corporation
+
+## Dataset Creation Date:
+9/24/2025
+
+## License/Terms of Use: 
+NVIDIA Evaluation Dataset License
+
+## Intended Usage:
+Researchers and developers seeking to evaluate LLMs on Professional Tasks. We recommend use of ProfBench as part of [Nemo Evaluator SDK](https://github.com/NVIDIA-NeMo/Evaluator), which supports a unified interface for evaluation across tens of benchmarks.
+
+## Dataset Characterization:
+** Data Collection Method<br>
+* [Hybrid: Human,  Synthetic, Automated]<br>
+
+** Labeling Method<br>
+* [Human] <br>
+
+## Dataset Format:
+Text.
+
+## Dataset Quantification:
+40 records
+
+Each record contains the following fields:
+
+- ID: Unique identifier for each sample
+- Domain: Chemistry PhD / Physics PhD / Finance MBA / Consulting MBA
+- Prompt: Instruction for the Large Language Model (LLM)
+- Rubrics: 15-59 unique criterion used to assess the final model output
+- Model Responses: 3 responses from OpenAI o3 / xAI Grok4 / DeepSeek R1-0528
+
+Some portions of this dataset were created with Grok.
+
+Total Storage: 1 MB.
+
+
+## Ethical Considerations:
+NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.   
+Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
+
+## Citation:
+
+If you found ProfBench helpful, please consider citing the below: 
+
+```
+@misc{wang2025profbenchmultidomainrubricsrequiring,
+      title={ProfBench: Multi-Domain Rubrics requiring Professional Knowledge to Answer and Judge}, 
+      author={Zhilin Wang and Jaehun Jung and Ximing Lu and Shizhe Diao and Ellie Evans and Jiaqi Zeng and Pavlo Molchanov and Yejin Choi and Jan Kautz and Yi Dong},
+      year={2025},
+      eprint={2510.18941},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2510.18941}, 
+}
+```