From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models
It addresses the problem of limited generalization from general to domain-specific reasoning for AI researchers and practitioners, but is incremental as it builds on existing trends in LLM training.
This study investigates the relationship between general reasoning capabilities in Large Language Models and their performance on domain-specific reasoning tasks, finding that while general reasoning improves, it does not fully translate to specialized domains, with domain-specific models outperforming general ones by up to 15% in accuracy.
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.