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# SciBench
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**SciBench** is a novel benchmark for college-level scientific problems
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problems sourced from instructional textbooks. The benchmark is designed to evaluate the complex reasoning capabilities,
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strong domain knowledge, and advanced calculation skills of LLMs.
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Please refer to our paper for full description:
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We developed an innovative **evaluation protocol** for a detailed analysis of reasoning abilities. This
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involves instructing LLMs to self-identify and categorize their errors within a predefined set of
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capabilities. This process offers a fine-grained understanding of where the models are falling short.
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## Data
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Each file is list of dictionary and can be extracted using following scripts.
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Each file stands for one textbook, which is fully elaborated in the paper.
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---
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license: mit
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# SciBench
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**SciBench** is a novel benchmark for college-level scientific problems sourced from instructional textbooks. The benchmark is designed to evaluate the complex reasoning capabilities,
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strong domain knowledge, and advanced calculation skills of LLMs.
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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
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## Citation
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If you find our paper useful, please cite our paper
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@inproceedings{wang2024scibench,
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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},
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title = {{SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models}},
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booktitle = {Proceedings of the Forty-First International Conference on Machine Learning},
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year = {2024},
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}
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```
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---
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license: mit
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---
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