# 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
---

arxiv:2307.10635 region:us
{}
TAGS
#arxiv-2307.10635 #region-us 

# 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

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. 



---
license: mit
---

# 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

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. 



---
license: mit
---
TAGS
#arxiv-2307.10635 #region-us 
 # 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

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. 



---
license: mit
---
passage: TAGS
#arxiv-2307.10635 #region-us 
# 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

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. 



---
license: mit
---