Prompt Engineering Institute

Introduction to Self-Consistency in LLMs

Self-consistency is an advanced prompting technique that builds on COT prompting. The aim here is to improve the naive greedy decoding using COT prompting by sampling multiple diverse reasoning paths and selecting the most consistent answers.

This can help boost the performance of COT prompting on tasks involving arithmetic and common sense reasoning. By utilizing a majority voting system, the AI model can arrive at more accurate and reliable answers.

Introduction to Chain of Thought (CoT) Prompting

Chain of Thought Prompting is a novel method developed by researchers at Google to enhance the reasoning capabilities of large language models. This approach breaks down multi-step problems into intermediate steps, allowing language models to tackle complex reasoning tasks that cannot be solved with standard prompting techniques. In this essay, we will discuss the benefits of Chain of Thought Prompting and review the experimental results obtained from its application.