We discuss a technique called self-consistency which enhances the reasoning capabilities of large language models (LLMs). This technique involves prompting an LLM to generate multiple reasoning paths for a question and then selecting the most consistent answer among these paths. This method improves the accuracy and reliability of LLMs, particularly for tasks requiring complex reasoning, such as arithmetic and commonsense reasoning.

ART addresses the limitations of traditional Chain-of-Thought (CoT) prompting by enabling LLMs to decompose tasks into multiple steps and utilize external resources, ultimately improving their performance on tasks requiring reasoning and complex problem-solving.

ReAct (Reason + Act) is a prompting technique that enhances the capabilities of Large Language Models (LLMs) by enabling them to reason, plan, and interact with external tools and data sources. This technique aims to overcome the limitations of traditional LLMs, which are restricted to their training data, leading to more accurate, reliable, and sophisticated applications.

A technique that enhances large language models (LLMs) by integrating them with external knowledge sources.

Large Language Models Are Human-Level Prompt Engineers.

Discover the Next Level of AI Interaction

AI That Thinks Like We Do:

Unraveling AI’s Thought Process