Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Discovered that prompting LLMs to generate intermediate step-by-step reasoning chains dramatically unlocks multi-step arithmetic, commonsense, and symbolic problem solving.

Directly guides architectural decisions, alignment strategy, and serving infrastructure for task-text-generation, task-question-answering.

Assumptions

• Empirical distribution regularity holds and target domain adheres to pretraining linguistic/visual support

Limitations

• Resource scaling, inference memory requirements, and alignment robustness vary with model size and hardware topology