Datasets:
metadata
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