In this Issue:

1. An overview of chain-of-thought(CoT) prompting as an LLM reasoning technique.

2. A review of Google’s original CoT paper.

3. An analysis of the ThinkGPT framework.

💡 ML Concept of the Day: Understanding Chain-Of-Thought Prompting

In our series about reasoning in LLMs, it is time to explore the most popular technique. Chain of thought(CoT) prompting is a technique proposed by Google Research that attempts to enable reasoning capabilities in LLMs.

Conceptually, CoT prompting attempts to emulate a human’s cognitive process when reasoning through a problem by decomposing it into intermediate reasoning steps. This method can be applied to language models of sufficient scale (approximately 100B parameters) and leads to improved performance on complex reasoning tasks. The method is based on prompting the model to produce intermediate reasoning steps, which mimics an intuitive thought process. Unlike standard prompting, chain-of-thought prompting does not require fine-tuning or modifying the model weights...