| # SciBench | |
| **SciBench** is a novel benchmark for college-level scientific problems consisting of _695_ | |
| 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 for full description: [SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models](https://arxiv.org/abs/2307.10635) | |
| We developed an innovative **evaluation protocol** for a detailed analysis of reasoning abilities. This | |
| involves instructing LLMs to self-identify and categorize their errors within a predefined set of | |
| capabilities. This process offers a fine-grained understanding of where the models are falling short. | |
| ## Data | |
| Each file is list of dictionary and can be extracted using following scripts. | |
| Each file stands for one textbook, which is fully elaborated in the paper. | |
| ``` | |
| subject='atkins' | |
| with open("./data/{}.json".format(subject), encoding='utf-8') as json_file: | |
| problems=json.load(json_file) | |
| ``` | |
| --- | |
| license: mit | |
| --- | |