Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models

This study examines how large language models (LLMs) use their training data to solve reasoning tasks, such as math problems, compared to answering factual questions. It found that while factual answers often come directly from specific training documents, reasoning tasks rely on general strategies learned from documents demonstrating how to solve similar problems. These strategies involve procedural knowledge, like using formulas or code, rather than simply retrieving answers. The research suggests that LLMs generalize reasoning tasks differently, synthesizing knowledge instead of copying it, offering insights into their capabilities and limitations in problem-solving and reasoning.

Grey Matterz Thoughts

LLMs rely on procedural knowledge for reasoning tasks, synthesizing strategies rather than retrieving direct answers. This highlights their unique approach to problem-solving and potential for learning generalizable reasoning methods.

Source: https://shorturl.at/jSsb4