探究大模型通用推理与领域专精推理的差距
From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models
- 对比通用推理与特定领域推理能力差异
- 发现通用能力无法直接迁移至专业任务
- 适合关注AI泛化极限的研究者阅读
近期大型语言模型（LLMs）在多个领域展现出卓越能力。然而，有效决策高度依赖强大的推理能力。推理是决策的基础，提供分析与逻辑框架以做出合理判断。它涉及信息分析、推断和基于逻辑或证据的结论得出。决策则在此基础上应用推理所得洞察，在多种选项中选择最优行动路径。两者共同构成实现目标的思维与行动循环。随着人工智能发展，训练大模型在通用推理上表现优异成为趋势。本研究探讨大模型的通用推理能力与其在特定领域推理任务中的表现之间的关联。
原文摘要 · Abstract (English)
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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