Large Language Models like GPT-3 demonstrate superior complex reasoning when using a 'chain of thought' (CoT) approach.

The chain of thought allows these models to articulate intermediate steps in problem-solving, enhancing their reasoning abilities.

Even with incorrect intermediate steps, the process of CoT benefits the model's performance.

Transformers require CoT to handle serial computations necessary for complex reasoning tasks, which they cannot efficiently perform otherwise.

Research suggests that with sufficient CoT steps, transformers could theoretically solve nearly any computationally difficult problem.

Experiments show that CoT significantly improves transformer performance on sequential arithmetic tasks, particularly for smaller or shallower models.

The chain of thought is a relatively straightforward technique that greatly expands the capabilities of transformer models to solve tasks requiring sequential logic.