The Chain-of-Thought prompting technique always improves model accuracy

Chain-of-thought (CoT) prompting has become a widely used strategy for working with large language and multimodal models. While CoT has been shown to improve performance across many tasks, determining the settings in which it is effective remains an ongoing effort.

Chain of thought prompting is highly effective in natural language processing tasks. It helps improve the accuracy and coherence of language models

This technique improves LLM performance by encouraging them to articulate their reasoning process, leading to more accurate answers

Chain-of-thought prompting enhances the ability of large language models to perform complex reasoning by elucidating their step-by-step thought processes

Chain-of-Thought prompting is a technique that improves the performance of language models by explicitly prompting the model to generate a step-by-step explanation or reasoning process before ...

offering significant benefits in various domains such as improved accuracy, transparency, and multi-step reasoning abilities

It does not positively impact performance for smaller LLMs and only yields performance gains when used with models of this size.

You can drastically improve the accuracy of such tasks using a technique called chain-of-thought (CoT) prompting