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--- |
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dataset_info: |
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features: |
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- name: Question |
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dtype: string |
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- name: Answer |
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dtype: string |
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- name: Answer_type |
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dtype: string |
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- name: Picture |
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dtype: image |
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splits: |
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- name: test |
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num_bytes: 5025005 |
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num_examples: 800 |
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download_size: 4949475 |
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dataset_size: 5025005 |
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configs: |
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- config_name: default |
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data_files: |
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- split: test |
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path: data/test-* |
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license: mit |
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task_categories: |
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- question-answering |
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language: |
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- en |
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tags: |
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- science |
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pretty_name: TheoremQA |
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size_categories: |
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- n<1K |
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--- |
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# Dataset Card for "TheoremQA" |
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## Introduction |
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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. |
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## How to use TheoremQA |
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``` |
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from datasets import load_dataset |
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dataset = load_dataset("TIGER-Lab/TheoremQA") |
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for d in dataset['test']: |
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print(d) |
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``` |
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## Arxiv Paper: |
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https://arxiv.org/abs/2305.12524 |
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|
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## Code |
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https://github.com/wenhuchen/TheoremQA/tree/main |
