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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.
from datasets import load_dataset
dataset = load_dataset("TIGER-Lab/TheoremQA")
for d in dataset['test']:
print(d)
https://arxiv.org/abs/2305.12524