| # SciBench | |
| **SciBench** is a novel benchmark for college-level scientific problems sourced from instructional textbooks. The benchmark is designed to evaluate the complex reasoning capabilities, | |
| strong domain knowledge, and advanced calculation skills of LLMs. | |
| Please refer to our [paper](https://arxiv.org/abs/2307.10635) or [website](https://scibench-ucla.github.io) for full description: SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models | |
| . | |
| ## Citation | |
| If you find our paper useful, please cite our paper | |
| ``` | |
| @inproceedings{wang2024scibench, | |
| author = {Wang, Xiaoxuan and Hu, Ziniu and Lu, Pan and Zhu, Yanqiao and Zhang, Jieyu and Subramaniam, Satyen and Loomba, Arjun R. and Zhang, Shichang and Sun, Yizhou and Wang, Wei}, | |
| title = {{SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models}}, | |
| booktitle = {Proceedings of the Forty-First International Conference on Machine Learning}, | |
| year = {2024}, | |
| } | |
| ``` | |
| --- | |
| license: mit | |
| --- | |