
## Introduction
We propose the first question-answering dataset driven by STEM theorems. We annotated 800 QA pairs covering 350+ theorems spanning across Math, EE&CS, Physics and Finance. The dataset is collected by human experts with very high quality. We provide the dataset as a new benchmark to test the limit of large language models to apply theorems to solve challenging university-level questions. We provide a pipeline in the following to prompt LLMs and evaluate their outputs with WolframAlpha.

## How to use TheoremQA
```
from datasets import load_dataset

dataset = load_dataset("wenhu/TheoremQA")

for d in dataset['test']:
  print(d)
```
To use the images, try to download images from images.zip in https://huggingface.co/datasets/wenhu/TheoremQA/blob/main/images.zip. The image is under the `Picture' field.

## Arxiv Paper:
https://arxiv.org/abs/2305.12524

## Code
https://github.com/wenhuchen/TheoremQA/tree/main
task_categories:question-answering size_categories:n<1K language:en license:mit question answering math science visual question answering arxiv:2305.12524 region:us
{"language": ["en"], "license": "mit", "size_categories": ["n<1K"], "task_categories": ["question-answering"], "pretty_name": "ThoeremQA", "tags": ["question answering", "math", "science", "visual question answering"]}
TAGS
#task_categories-question-answering #size_categories-n<1K #language-English #license-mit #question answering #math #science #visual question answering #arxiv-2305.12524 #region-us 


## Introduction
We propose the first question-answering dataset driven by STEM theorems. We annotated 800 QA pairs covering 350+ theorems spanning across Math, EE&CS, Physics and Finance. The dataset is collected by human experts with very high quality. We provide the dataset as a new benchmark to test the limit of large language models to apply theorems to solve challenging university-level questions. We provide a pipeline in the following to prompt LLMs and evaluate their outputs with WolframAlpha.

## How to use TheoremQA

To use the images, try to download images from URL in URL The image is under the 'Picture' field.

## Arxiv Paper:
URL

## Code
URL
## Introduction
We propose the first question-answering dataset driven by STEM theorems. We annotated 800 QA pairs covering 350+ theorems spanning across Math, EE&CS, Physics and Finance. The dataset is collected by human experts with very high quality. We provide the dataset as a new benchmark to test the limit of large language models to apply theorems to solve challenging university-level questions. We provide a pipeline in the following to prompt LLMs and evaluate their outputs with WolframAlpha. ## How to use TheoremQA

To use the images, try to download images from URL in URL The image is under the 'Picture' field. ## Arxiv Paper:
URL ## Code
URL
TAGS
#task_categories-question-answering #size_categories-n<1K #language-English #license-mit #question answering #math #science #visual question answering #arxiv-2305.12524 #region-us 
 ## Introduction
We propose the first question-answering dataset driven by STEM theorems. We annotated 800 QA pairs covering 350+ theorems spanning across Math, EE&CS, Physics and Finance. The dataset is collected by human experts with very high quality. We provide the dataset as a new benchmark to test the limit of large language models to apply theorems to solve challenging university-level questions. We provide a pipeline in the following to prompt LLMs and evaluate their outputs with WolframAlpha. ## How to use TheoremQA

To use the images, try to download images from URL in URL The image is under the 'Picture' field. ## Arxiv Paper:
URL ## Code
URL
passage: TAGS
#task_categories-question-answering #size_categories-n<1K #language-English #license-mit #question answering #math #science #visual question answering #arxiv-2305.12524 #region-us 
## Introduction
We propose the first question-answering dataset driven by STEM theorems. We annotated 800 QA pairs covering 350+ theorems spanning across Math, EE&CS, Physics and Finance. The dataset is collected by human experts with very high quality. We provide the dataset as a new benchmark to test the limit of large language models to apply theorems to solve challenging university-level questions. We provide a pipeline in the following to prompt LLMs and evaluate their outputs with WolframAlpha.## How to use TheoremQA

To use the images, try to download images from URL in URL The image is under the 'Picture' field.## Arxiv Paper:
URL## Code
URL