From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models

• URL: http://arxiv.org/abs/2506.21580v1
• Date: Mon, 16 Jun 2025 21:20:08 GMT
• Title: From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models
• Authors: Dana Alsagheer, Yang Lu, Abdulrahman Kamal, Omar Kamal, Mohammad Kamal, Nada Mansour, Cosmo Yang Wu, Rambiba Karanjai, Sen Li, Weidong Shi,
• Abstract summary: Reasoning is the foundation for decision-making.<n>As AI technology evolves, there is a growing trend to train LLMs to excel in general reasoning.<n>This study explores how the general reasoning capabilities of LLMs connect to their performance in domain-specific reasoning tasks.
• Score: 9.678141197095023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in Large Language Models (LLMs) have demonstrated remarkable capabilities in various domains. However, effective decision-making relies heavily on strong reasoning abilities. Reasoning is the foundation for decision-making, providing the analytical and logical framework to make sound choices. Reasoning involves analyzing information, drawing inferences, and reaching conclusions based on logic or evidence. Decision-making builds on this foundation by applying the insights from reasoning to select the best course of action among alternatives. Together, these processes create a continuous cycle of thought and action aimed at achieving goals effectively. As AI technology evolves, there is a growing trend to train LLMs to excel in general reasoning. This study explores how the general reasoning capabilities of LLMs connect to their performance in domain-specific reasoning tasks.

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