Chain of Thought Prompting – Simple Yet Powerful Technique to Harness GPT3

The emergence of large language models, such as ChatGPT, has revolutionized the field of natural language processing and has paved the way for new applications and advancements in artificial intelligence.

In the past, language models were limited by the size of the data they were trained on and the computational resources available. However, with advances in hardware and the availability of large amounts of data, researchers have been able to train much larger language models that can generate human-like text and perform a wide range of language tasks with unprecedented accuracy.

One of the most well-known large language models is ChatGPT, developed by OpenAI. ChatGPT is a conversational AI model trained on a diverse range of text, including books, websites, and social media. It uses a transformer architecture and is capable of generating text that is coherent and context-sensitive.

ChatGPT has been used for a variety of applications, including chatbots, language translation, question-answering, and summarization. It has been integrated into many platforms, such as customer service chatbots, and has proven to be an effective tool for automating and streamlining communication.

A simple technique to improve problem solving capabilities of LLM such as GPT3 is to use chain of thought prompting . In this technique a human labeler feeds in intermediate steps in response prompts (see example below), like a teacher encourages its student to explain the reasoning. The model can then can learn to reason on unseen problems, and improve its ability to answer questions that require multi step reasoning. This also helps a user to understand the thought process and how the model derived the answer.

Blog by Jeff Dean, mentions power of chain of prompt reasoning