README.md

---
dataset_info:
  features:
  - name: Question
    dtype: string
  - name: Answer
    dtype: string
  - name: Answer_type
    dtype: string
  - name: Picture
    dtype: image
  splits:
  - name: test
    num_bytes: 5025005
    num_examples: 800
  download_size: 4949475
  dataset_size: 5025005
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
license: mit
task_categories:
- question-answering
language:
- en
tags:
- science
pretty_name: TheoremQA
size_categories:
- n<1K
---
# Dataset Card for "TheoremQA"

## 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("TIGER-Lab/TheoremQA")

for d in dataset['test']:
  print(d)
```

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

## Code
https://github.com/wenhuchen/TheoremQA/tree/main