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Logical Reasoning Capabilities of Large Language Models: A Comparative Evaluation on GPQA Dataset

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All tested models outperformed human experts in overall accuracy, showing the potential of large language models in reasoning tasks.
Models using Chain-of-Thought mechanisms demonstrated higher accuracy, signifying the importance of advanced reasoning strategies.
Physics tasks showed strong performance, while chemistry questions with complex organic inference revealed weaknesses in model capabilities.
Increased processing time did not improve accuracy, indicating inefficiencies in reasoning pathways of the models.

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This evaluation compares logical reasoning performance by five LLMs on the GPQA dataset, indicating advances in accuracy and response time.

Bo Wan (2025) studied this question.

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This evaluation compares logical reasoning performance by five LLMs on the GPQA dataset, indicating advances in accuracy and response time.