GPT-3's Impressive Analogical Reasoning Skills: A Comparison with Human Reasoners

Hello Everyone,

I hope you had a good Friday, before the weekend, brace yourselves for an exciting end to the week as we delve into GPT-3's impressive analogical reasoning skills and how they compare to human reasoning abilities.

Are you ready to be amazed by the capabilities of artificial intelligence? In the paper I would like to present to you, researchers pitted a large language model called GPT-3 against human subjects in a series of tasks designed to test analogical reasoning skills.?

The results were surprising - GPT-3 often outperformed the human subjects, demonstrating an emergent ability to solve new problems without being directly trained on them. Keep reading to learn more about this groundbreaking research and its implications for the future of AI.

Effectively, large language models (LLMs) are very advanced computer programs that can understand and process natural language (the way people communicate using words and sentences). They are trained on a very large amount of text data, which allows them to understand and generate language in a way that is similar to how humans do.?

Recently, there has been a lot of interest in whether these large language models might be able to exhibit human-like reasoning abilities, especially when it comes to understanding and solving new problems (called "zero-shot" reasoning or learning, one of my favorite type of machine learning family, see below for more information about ZSL [*]).

In this study, researchers compared the abilities of a large language model called GPT-3 with human subjects on a variety of tasks that involved analogical reasoning (finding relationships between different things).?

These tasks included a special kind of problem-solving task called "matrix reasoning," which is similar to a type of test called Raven's Progressive Matrices which is often used to measure abstract reasoning skills in humans.?

The researchers found that GPT-3 was very good at solving these kinds of problems, sometimes even outperforming human subjects. This suggests that large language models like GPT-3 might have developed an ability to find solutions to new problems without being directly trained on them, which is a similar ability to humans' ability to reason by analogy.

• Link to Arxiv: https://arxiv.org/abs/2212.09196v1
• Link to the pdf of the paper: https://arxiv.org/pdf/2212.09196v1.pdf?

[*] Zero-shot learning (ZSL) is a type of artificial intelligence (AI) that allows a computer program to solve a problem or perform a task without any direct training on that specific problem or task. Instead, the program uses its general knowledge and understanding of the world to figure out how to solve the problem. This ability is similar to how humans can apply their knowledge and understanding to new situations and problems that they have not encountered before.

For example, if an AI system has been trained to recognize dogs, it might be able to recognize a new breed of dog that it has never seen before, using its understanding of the characteristics that define a dog (such as four legs, a tail, and fur) to identify the new breed as a dog.

One of the key characteristics of zero-shot reasoning is that it requires the AI system to be able to generalize from its training data and apply that knowledge to novel situations. This ability is considered to be a key aspect of human intelligence, and researchers are interested in developing AI systems that can demonstrate similar capabilities.

Zero-shot learning is an important area of research in AI because it allows AI systems to be more flexible and adaptable, and to learn new things more quickly. It has the potential to significantly expand the capabilities of AI systems and make them more useful in a wide range of applications (Try by yourself with ChatGPT: https://chat.openai.com/chat).