Can We Understand How Large Language Models (LLMs) Reason

Scientific exploration is underway into how the Reasoning ability that emerges when large language models (LLMs) solve complex problems arises within the model.

The main issues in current research are as follows.

• Interpretability: Attempts to analyze how logical steps are formed through the model's weights and activation patterns
• Emergent Abilities: Debate over the phenomenon where certain reasoning abilities suddenly appear as model scale increases
• Mechanistic Interpretability: Identifying how individual neurons or circuits in a neural network learn specific logical rules

However, due to the model's massive parameter scale and nonlinear characteristics, technical limitations still exist in intuitively connecting human logical structures with the model's computational process.