From Claude3:
How does a large language model learn to reason?
Large language models (LLMs) like myself don't learn to reason in the same way humans do. Instead, we develop reasoning-like capabilities through statistical pattern recognition during training on vast amounts of text data. Here's a brief overview of how this works:
Pattern recognition: Through this training, the model learns to predict likely sequences of words and concepts based on context.
Statistical correlations: The model develops a complex web of statistical relationships between words, phrases, and concepts.
Emergent abilities: As the model grows in size and is exposed to more data, it can start to exhibit behaviors that resemble reasoning, such as:
Limitations: However, LLMs don't truly understand or reason in a human sense. They're essentially very sophisticated pattern matching systems.
This is a simplified explanation of a complex topic.
Basically LLMs do not reason at all. Instead they use pattern recognition and statistical probabilities to generate a response.
The more data it has the better.