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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2506.21580 |
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| _version_ | 1866912452820598784 |
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| author | Alsagheer, Dana Lu, Yang Kamal, Abdulrahman Kamal, Omar Kamal, Mohammad Mansour, Nada Wu, Cosmo Yang Karanjai, Rambiba Li, Sen Shi, Weidong |
| author_facet | Alsagheer, Dana Lu, Yang Kamal, Abdulrahman Kamal, Omar Kamal, Mohammad Mansour, Nada Wu, Cosmo Yang Karanjai, Rambiba Li, Sen Shi, Weidong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_21580 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models Alsagheer, Dana Lu, Yang Kamal, Abdulrahman Kamal, Omar Kamal, Mohammad Mansour, Nada Wu, Cosmo Yang Karanjai, Rambiba Li, Sen Shi, Weidong Computation and Language Artificial Intelligence Computers and Society 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. |
| title | From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models |
| topic | Computation and Language Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2506.21580 |