Papers › TheoremQA: A Theorem-driven Question Answering dataset

TheoremQA: A Theorem-driven Question Answering dataset

21 May 2023arXiv:2305.12524archive 2025-07-28

Wenhu Chen, Ming Yin, Max Ku, Pan Lu, Yixin Wan, Xueguang Ma, Jianyu Xu, Xinyi Wang, Tony Xia

The recent LLMs like GPT-4 and PaLM-2 have made tremendous progress in solving fundamental math problems like GSM8K by achieving over 90% accuracy. However, their capabilities to solve more challenging math problems which require domain-specific knowledge (i.e. theorem) have yet to be investigated. In this paper, we introduce TheoremQA, the first theorem-driven question-answering dataset designed to evaluate AI models' capabilities to apply theorems to solve challenging science problems. TheoremQA is curated by domain experts containing 800 high-quality questions covering 350 theorems (e.g. Taylor's theorem, Lagrange's theorem, Huffman coding, Quantum Theorem, Elasticity Theorem, etc) from Math, Physics, EE&CS, and Finance. We evaluate a wide spectrum of 16 large language and code models with different prompting strategies like Chain-of-Thoughts and Program-of-Thoughts. We found that GPT-4's capabilities to solve these problems are unparalleled, achieving an accuracy of 51% with Program-of-Thoughts Prompting. All the existing open-sourced models are below 15%, barely surpassing the random-guess baseline. Given the diversity and broad coverage of TheoremQA, we believe it can be used as a better benchmark to evaluate LLMs' capabilities to solve challenging science problems. The data and code are released in https://github.com/wenhuchen/TheoremQA.

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Tasks

MathQuestion Answering

Datasets

Introduced by this paper, per the archive.

TheoremQA

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Natural Questions TheoremQA GPT-4 (PoT) Accuracy 52.4 #1 of 19 Archive leaderboard report
Natural Questions TheoremQA GPT-4 (CoT) Accuracy 43.8 #2 of 19 Archive leaderboard report
Natural Questions TheoremQA GPT-3.5-turbo (PoT) Accuracy 35.6 #3 of 19 Archive leaderboard report
Natural Questions TheoremQA PaLM-2-unicorn (CoT) Accuracy 31.8 #6 of 19 Archive leaderboard report
Natural Questions TheoremQA GPT-3.5-turbo (CoT) Accuracy 30.2 #7 of 19 Archive leaderboard report
Natural Questions TheoremQA Claude-v1 (PoT) Accuracy 25.9 #10 of 19 Archive leaderboard report
Natural Questions TheoremQA Claude-v1 (CoT) Accuracy 24.9 #11 of 19 Archive leaderboard report
Natural Questions TheoremQA code-davinci-002 Accuracy 23.9 #12 of 19 Archive leaderboard report
Natural Questions TheoremQA Claude-instant (CoT) Accuracy 23.6 #13 of 19 Archive leaderboard report
Natural Questions TheoremQA text-davinci-003 Accuracy 22.8 #14 of 19 Archive leaderboard report
Natural Questions TheoremQA PaLM-2-bison (CoT) Accuracy 21.0 #15 of 19 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutGPT-4Label SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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