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(original paper) "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models" https://arxiv.org/abs/2201.11903
Chain-of-Thought prompting is a variant of few-shot prompting in which the language model is shown a few examples within it's prompt of how it should break down it's task in a logical way.
Example prompt (from the original paper): Q: Take the last letters of the words in "Elon Musk" and concatenate them. A: The last letter of "Elon" is "n". The last letter of "Musk" is "k". Concatenating them is "nk". The answer is nk. Q: Take the last letters of the words in "Larry Page" and concatenate them. A: The last letter of "Larry" is "y". The last letter of "Page" is "e". Concatenating them is "ye". The answer is ye. Q: Take the last letters of the words in "Sergey Brin" and concatenate them. A: The last letter of "Sergey" is "y". The last letter of "Brin" is "n". Concatenating them is "yn". The answer is yn. Q: Take the last letters of the words in "Bill Gates" and concatenate them. A:
In the paper "Large Language Models are Zero-Shot Reasoners" (https://arxiv.org/abs/2205.11916), the authors observed that simply appending the text "Let's think step by step" to the end of the model prompt notably improved model performance.
- [[Applied Large Language Model Concepts]]
- [[One-Shot and Few-Shot Learning]]
- [[LLM ReAct Prompting]]
- (paper) Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (https://arxiv.org/abs/2201.11903)
- https://www.promptingguide.ai/techniques/cot