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

TL;DR

This paper investigates the connection between large language models' general reasoning abilities and their performance in domain-specific reasoning tasks, highlighting the limits of their generalization capabilities.

Contribution

It provides an analysis of how general reasoning skills in LLMs translate to domain-specific tasks and identifies the boundaries of their generalization.

Findings

General reasoning skills do not always transfer effectively to domain-specific tasks.

LLMs exhibit limitations in domain adaptation for reasoning.

Performance varies significantly across different reasoning domains.

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.