chainofthought

Chain of thought refers to the explicit sequence of reasoning steps that connect a problem to a solution. In cognitive science, it describes the internal thoughts and deliberations people use to reach conclusions. In artificial intelligence, chain-of-thought (CoT) denotes prompting methods and model outputs that reveal or simulate intermediate reasoning steps before presenting a final answer. The approach is used to improve performance on tasks that require multi-step reasoning, such as arithmetic, logic, or planning, particularly with large language models.

Origin and development: The concept gained prominence with the rise of chain-of-thought prompting in large language models, notably in work published around 2022, which showed that guiding models to generate step-by-step reasoning could yield higher accuracy on complex problems. Researchers have since explored prompting strategies, the verification of produced steps, and methods to balance interpretability with the risk of exposing internal prompts or policies.

Mechanism and use: In practice, prompts are designed to elicit intermediate steps before the final answer, encouraging the model to decompose problems into smaller parts. This can improve solvability and provide interpretable reasoning traces. CoT is applied in math problem solving, planning tasks, code reasoning, and debugging.

Limitations and considerations: Chain-of-thought outputs can be incorrect or partially hallucinated even if the final answer appears plausible. The approach often depends on model size and well-crafted prompts, and may be less effective for smaller models. Evaluating the correctness of intermediate steps is challenging, and care is needed to avoid unintended leakage of sensitive instructions.

See also: chain-of-thought prompting; few-shot learning; prompt engineering.