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Improving Chain-of-Thought Reasoning
The accuracy of chain-of-thought reasoning in language models can be enhanced through various strategic modifications. These include sampling multiple distinct reasoning paths to find consensus (self-consistency), diversifying the provided few-shot demonstrations, and reducing complex problems into smaller, manageable sub-problems.
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Example of a Model Initiating Chain-of-Thought Reasoning
A user is trying to solve a word problem using a large language model. They use two different prompts and receive two different responses.
Prompt 1: "A farmer has 5 pens, and each pen holds 8 chickens. The farmer buys 10 more chickens. How many chickens does the farmer have in total?" Response 1: "The farmer has 50 chickens in total."
Prompt 2: "A farmer has 5 pens, and each pen holds 8 chickens. The farmer buys 10 more chickens. How many chickens does the farmer have in total? Let
Modifying a Prompt for Step-by-Step Reasoning
Improving AI Reasoning for a Multi-Step Problem
Improving Chain-of-Thought Reasoning
Zero-Shot Chain-of-Thought Reasoning
Multimodal Chain-of-Thought Reasoning